Neurocomputing 328 (2019) 16–28

Contents lists available at ScienceDirect

Neurocomputing

journal homepage: www.elsevier.com/locate/neucom

Decade progress of palmprint recognition: A brief survey

Dexing Zhong

∗, Xuefeng Du, Kuncai Zhong

School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China

a r t i c l e i n f o

Article history:

Received 4 November 2017

Revised 14 February 2018

Accepted 22 March 2018

Available online 20 August 2018

Keywords:

Palmprint recognition

ROI (region of interest)

Feature extraction

Matching

Fusion

a b s t r a c t

As an advanced research topic in biometrics techniques, palmprint recognition has been fully studied for

more than 20 years. Due to its superiority to other biological features, i.e. high recognition accuracy and

convenience for practical application, many research achievements on palmprint have emerged recently,

especially in the past decade. This paper presents a comprehensive overview of recent research progress

of palmprint recognition as well as the basic background knowledge for it. In addition, it mainly focuses

on data acquisition, database, preprocessing, feature extraction, matching and fusion. Ultimately, we dis-

cuss the challenges and future perspectives in palmprint recognition for further works.

© 2018 Elsevier B.V. All rights reserved.

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1. Introduction

Previously, people tended to use ID cards, keys and pass-

word for personal identification. With the rapid development of

information technology and online financial activities, inherent

problems of the above identification methods, i.e. the loss, du-

plication attack and misappropriation, make them incompatible

to the requirements for accurate and reliable recognition per-

formance. However, biometrics based on the physiological and

behavioral characteristics of a human provide a convenient and

stable solution [1] . To date, researchers have applied iris [2] , skin

[3] , palm vein [4] , palmprint [5] and other features [6] in personal

verification and authentication. As one of popular biometric meth-

ods, palmprint has some outstanding strengths compared with

other biological characteristics [7,8] . A palmprint image consists

of much discriminative information such as the ridges and palm

lines, which can ensure the recognition accuracy [9] . Consequently,

palmprint recognition has attracted many researchers’ attention;

meanwhile it has undergone numerous developments lately.

Until now, a number of researches have been conducted on

various aspects of palmprint recognition and many valuable view-

points to enhance its performance have been proposed [10–12] .

In order to summarize the periodical developments of this re-

search field, a timely survey is necessary for subsequent research

works. In 2009, Kong et al. [13] accomplished a survey describ-

ing particularly the capture devices, preprocessing, verification al-

gorithms, palmprint-related fusion, and recognition measures for

∗ Corresponding author.

E-mail address: bell@xjtu.edu.cn (D. Zhong).

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https://doi.org/10.1016/j.neucom.2018.03.081

0925-2312/© 2018 Elsevier B.V. All rights reserved.

rivacy protection. Recently, several new effective directions have

merged for person identification. Therefore, in this paper, we con-

entrate on decade progress of palmprint recognition, including

ata acquisition, preprocessing, feature extraction, matching and

usion. In addition to revealing the state-of-art algorithms, we at-

empt to provide the challenges and future perspectives in palm-

rint recognition for further works.

Novel methods appearing in the decade can be roughly clas-

ified into five categories: contactless palmprint recognition,

igh-resolution palmprint recognition, multispectral palmprint

ecognition, 3D palmprint recognition and the fusion with other

iometrics. Contactless way refers to employing some peg-free

ystems without any constraints to acquire images. It is more

cceptable by users and can simultaneously solve the hygiene

roblem as well [14,15] . Zhang et al. [16] presented a high quality

cquisition device, collected the largest contactless palmprint im-

ge database and proposed collaborative representation CompCode

CR_CompCode) for recognition. A stereo camera [12] was set up

o carry out pose correction and hand segmentation for portable

ecognition.

High resolution refers to that the resolution of an image is up

o 400 dpi or more as illustrated in Fig. 1 , which is very suitable

or forensics and legitimate application [13] . There are two ma-

or methods for high-resolution palmprint recognition: minutiae-

ased and regional-fusion based methods [17,18] . Feng et al.

17] put forward the Gabor Amplitude-Phase model and Adaboost

lgorithm for palmprint representation. Wang et al. [18] proposed

matching strategy based on local fusion. This strategy used the

egion of main palmprint segmented by palm lines to reduce the

rror rate significantly.

D. Zhong et al. / Neurocomputing 328 (2019) 16–28 17

Fig. 1. Palmprint features in (a) a high-resolution image and (b) a low-resolution

image [13] .

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In terms of multispectral methods, they use features extracted

nder distinctive spectral wavelengths for identification to improve

he accuracy and anti-spoof capability [5] . Guo et al. [19] analyzed

yperspectral palmprint data to determine the optimal number of

pectral bands and obtained the most typical bands to establish

heir recognition system.

So far, 3D palmprint recognition has undergone essential

rogress as well. While 2D images can be counterfeited by law-

reakers or contaminated by noise [20] , 3D images contain more

epth information and are gradually used to improve the robust-

ess of recognition. Zhang et al. [21] exploited the 3D structural

nformation of the palm and proposed the structured light imag-

ng to establish palmprint datasets. Then they extracted mean and

aussian curvature image, surface type for classification that are

ore stable to illumination variations.

In addition, some researchers found that using multimodal bio-

etrics could significantly improve the recognition rate because

ifferent features serve as mutual supplement to each other. Thus,

ui et al. [22] applied principal component analysis (PCA) and

wo-phase test sample representation (TPTSR) to present the fu-

ion scheme of 2D and 3D features. Face and palmprint features

re fused by SDA-GSVD (subclass discriminant analysis-generalized

ingular value decomposition) [23] , which outperforms some re-

ated multimodal recognition methods.

To our best knowledge, a classic palmprint recognition pro-

ess is composed of five sections: palmprint image acquisition,

atabase, preprocessing, feature extraction and matching that are

emonstrated in Fig. 2 .

The acquisition device obtains palmprint images of different

ualities that are consistent with subsequent recognition. The re-

ion of interest (ROI) is the core of preprocessing stage. Commonly

sed algorithms are the reference coordinate system method,

hich is showed in Fig. 3 . In view of the feature extraction, several

inds of algorithms were proposed [5] , such as subspace methods,

earning methods, line-based and coding-based approaches. Each

ethod extracts features from a global or local scope, and each has

ts own advantages. The matching process matches testing samples

ith other samples in the database based on a certain predeter-

ined matcher.

The rest of the paper focuses on the development of palm-

rint recognition in the recent decade within five sections.

ection 2 sums up emerging acquisition devices and correspond-

ng datasets. It also retrospects distinctive preprocessing algo-

ithms. Section 3 explains most of novel feature extraction meth-

ds while demonstrating matching means. Section 4 focuses on

he palmprint-related fusion. Section 5 discusses several summa-

ion points and offers future directions for further development in

etail.

. Image acquisition and preprocessing

.1. Image acquisition

When it comes to the acquisition algorithms, each of them is

ased on a specific database and a concrete application direction.

ue to the changeable environment in the real world, many effec-

ive algorithms proposed in the ideal acquisition condition are not

uitable for practical application of palmprint recognition. There-

ore, it is important to set up different databases to simulate dif-

erent conditions and test whether a particular algorithm fits into

he research environment. Then some modifications can be carried

ut to achieve a better experimental result.

In the recent decade, great deals of new databases have been

stablished, such as the examples in [4,12,16,24–31] . Except that

ome databases use traditional cameras and classic acquisition

ays [25,29,31] , i.e. CCD-based (charge-coupled device) scanners,

igital cameras, video cameras, to collect palmprint images, many

atabases are built adopting new devices that capture images on

ifferent platforms [4,27,32] . For instance, Aykut et al. [33] used

CCD camera, direct current (DC) auto iris lens, hand placement

latform and uniform LED (light emitting diode) light sources to

ccomplish online palm image acquisition, which was revealed in

ig. 4 .

In all image types, 2D palmprint data is the most widely used

ata because it is easily accessible and can be handled easily

oo. Meanwhile, there are also many databases containing other

almprint information, such as 3D [12,21,24,34,35] , multispectral

19,26,30] , and minutiae [25] . All three sorts of these images are

emonstrated in Figs. 5 and 6 .

To describe the developmental process of acquisition techniques

ore clearly, we list representative results in Table 1 , showing the

ummary of different databases established in recent decade. Ap-

roximately 25 new palmprint databases have been established,

ringing the total number of samplers to 4,200. It is sufficient to

imulate real and different conditions, which is a basic require-

ent in image obtaining process.

.2. Preprocessing

Preprocessing is the foundation for feature extraction and

atching. The quality of preprocessing has a significant impact on

he outcomes of recognition. In this paper, we mainly focus on the

evelopment of algorithms to extract ROI. Because it is the major

tep in preprocessing stage apart from other procedures like image

nhancement, image filtering and so on.

In the recent decade, distance is the most momentous measur-

ng target in ROI extraction. This method keeps a fixed pixel dis-

ance between the edge of ROI and the connection line of the val-

ey points [4,21,27,44,45] . However, because of the size variety of

almprint images, valuable area for feature extraction will not be

xtracted accurately if researchers only apply the distance princi-

le. Thus, the result of recognition cannot reach the highly desired

tandard. Consequently, other measures are employed lately, such

s ratio [19,46] and angle [47,48] .

18 D. Zhong et al. / Neurocomputing 328 (2019) 16–28

Fig. 2. The flow-process diagram of palmprint recognition system.

Fig. 3. Classic steps of preprocessing: (a) original image, (b) binary image, (c) boundary tracking, (d) building a coordinate system, (e) extracting the central part and (f) ROI

sample [19] .

Fig. 4. External view of palm image acquisition system and a hand placed to the platform [33] .

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The principle of ratio is that the size of ROI accounts for a fixed

ratio in palmprint images. Angle principle makes use of the princi-

ple that ROI boundary point-valley point connection line and val-

ley point connection line have a constant angle. And according to

many experiments, the angle selected to be 45 ° or 60 ° is suitable

for precise feature extraction [47] . Paradigms of three distinctive

methods are illustrated in Figs. 7 –9 . These above approaches can

greatly decrease the error rate caused by variance of image size,

rotation and other environmental defects. Due to occurrence of

dverse factors in an image, like overlapping, different number of

alley points will result in different consequences of ROI extrac-

ion. Most of articles used 2 ∼6 valley points [4,19,21,27,44,45] and

here were also some using 12 [49] and 15 [33] valley points. Av-

rage number is five to our best knowledge.

Table 2 shows the summary of ROI extraction algorithms. A ten-

ency can be discovered from the chart, ratio and distance were

ore widely used nowadays [43,46,49] , and the fusion of ratio and

istance were applied more often in recent 2 years [16,51] .

D. Zhong et al. / Neurocomputing 328 (2019) 16–28 19

Fig. 5. (a) 2D palmprint image, (b) Extracted ROI of 2D image [36] and the minutiae of high resolution image [25] . .

Fig. 6. The above row shows the 3D palmprint images. The below row shows the 2D palmprint images [37] .

Table 1

Summary of different databases established in the recent decade. Device expresses whether the re-

search uses new device or platform (Y is yes and N is not). Data is the kind of information obtained

from the database. Number refers to the number of pictures that belong to one people in the dataset.

Device(Y/N) Data(2D/3D/Multispectral/Minutiae) Number Year Article

N 2D 40 2008 [29]

N 2D 346 2008 [29]

Y 2D 120 2008 [4]

N 2D 146 2009 [38]

Y 2D 150 2009 [32]

Y 3D 260 2009 [21]

N Minutiae \ 2011 [25]

Y Multispectral \ 2011 [26]

N 2D \ 2011 [28]

Y Multispectral 190 2012 [19]

Y 2D 100 2012 [27]

Y 2D 193 2012 [39]

Y Multispectral 500 2012 [30]

N 2D 500 2013 [31]

Y 2D 40 2013 [33]

Y 2D 20 2013 [40]

Y 3D 200 2013 [35]

N 2D 100 2014 [41]

N 2D 60 2015 [42]

N 2D 60 2015 [42]

Y 3D 100 2015 [34]

N 2D 75 2015 [43]

Y 3D 138 2017 [12]

N 2D & 3D 260 2017 [24]

Y 2D 600 2017 [16]

Fig. 7. Five steps of preprocessing based on the distance [50] .

20 D. Zhong et al. / Neurocomputing 328 (2019) 16–28

Fig. 8. ROI location using the ratio method: (a) palm width L determination, (b) ROI

creation with [OO1] = 1/10 L and [E1E2] = 2/3 L [46] .

Fig. 9. ROI extraction using the angle method: (a) Scanned image. (b) Binarized

image. (c) Hand contour and reference points. (d) Relevant points and palmprint.

(e) Palmprint region in gray scale hand image. (f) Extracted palmprint [47] . .

Table 2

Summary of ROI extraction. Ratio, angle and distance express the prin-

ciple of ROI extraction. The number in the second line denotes the

number of valley points utilized.

Ratio/Angle/Distance Valley Points Year Article

Distance 2 2008 [4]

Distance 2 2009 [45]

Distance 6 2009 [21]

Distance 2 2011 [44]

Ratio 2 2012 [19]

Distance 3 2012 [27]

Angle 6 2012 [47]

Angle 6 2013 [48]

Ratio 4 2014 [46]

Distance 12 2014 [49]

Distance 4 2015 [43]

Ratio & Distance 5 2016 [51]

Ratio & Distance 4 2017 [16]

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3. Feature extraction and matching

3.1. Feature extraction

Feature is the main index for comparison. The work of feature

extraction is for the sake of maximizing difference of different peo-

ple and similarity of the same ones. The algorithms to extract fea-

tures have developed rapidly in recent decade, not only about the

types of feature, but also about ways to record feature accurately

and effectively.

3.1.1. Discussion about the extracted features

The principal features in the recent decade are texture

[39,41,44,4 8,4 9,52,53] , orientation [45,50,52,54–57] , discrimination

[15,24,58,59] and frequency [12,60] . New developments also

emerged synchronously [16,61] . For instance, single orientation

was not suitable for accurate recognition. Therefore, the double-

orientation [61] was proposed to fully extract features. In addi-

ion to primary kinds, many novel features also appeared, such

s Laplacianpalm feature [4] , LRV (Local relative variance) [38] ,

MP (Gabor magnitude and phase) information [62] , Blur invari-

nt phase [43] , energy information [63] . Multi-feature is also the

rend of feature extraction. Distinctive kinds of feature were fused

nto one feature entirety and each one is complementary to an-

ther [12,14,29,32,35,64–68] .

.1.2. Discussion about feature processing methods

The way to record feature determines the utilization of fea-

ure information. According to the researches in recent decade,

xisting methods can be divided into three modes, encod-

ng [21,26,32,38,45,54,61] , photo [4,28,29,57,64,69,70] and learning

60,71–81] . Specially, photo approach uses image information di-

ectly, for example, PCA and linear discriminant analysis (LDA). It

as uncomplicated to extract and compare the features intuition-

lly. In order to better explain photo techniques, we also divide

t into three distinctive sections [82,83] , namely structure-based,

tatistics-based and subspace-based algorithms.

1) Encoding-based algorithms

Encoding way transfers images to coded information. It is easy

nd time-efficient to deal with in processors. The form of matrix

erivation can reduce space complexity meanwhile. Generic coding

eans that a palmprint image is first filtered using a predefined

lter, then encoded according to a certain principle. Afterwards,

imilarity degree can be obtained using binary arithmetic oper-

tion. From the emergence of IrisCode, coding techniques have

eveloped rapidly. PalmCode, Competitive code, Fusion code and

rdinal Code were proposed successively. In the recent decade,

ore attention was paid to the orientation information of a palm-

rint rather than the phase content. Moreover, more researchers

re concerned about the robustness and modification of filters’

esign, coding scheme and classification guideline.

In order to study the influence of the number and orientation of

abor filters, a modified fuzzy C-means cluster algorithm [45] was

roposed to determine the orientation of each Gabor filter. Kim

t al. [84] designed a new hybrid approach using both the line

nd slope orientation to reduce the effect of lighting conditions

n position information. The information is for pixels that are not

round palm line, which greatly optimizes robust line orientation

ode (RLOC) and binary orientation co-occurrence vector (BOCV).

n 2012, PalmCode was improved using the Gabor wavelet con-

olved with the palmprint image [39] , then they employed local

inary pattern (LBP) to code the relationship, which is between the

agnitude of wavelet response at the central pixel and that of its

eighbors. There are some analogous modifications [53,85] . Con-

erning the progress of encoding scheme, Riesz transform [68] was

tilized to encode local patterns of palmprint images in two ways.

t is thus more suitable for time critical applications. In addi-

ion, there are some other innovative examples of coding schemes

16,54,61] .

2) Structure-based methods

Structure-based methods were traditional recognition tech-

iques, which were transplanted from fingerprint recognition. The

ey of them is the usage of edge detection algorithm to extract

he orientation and location information of ridges, lines or feature

oints. Nevertheless, they have many drawbacks. For example, they

eplace real palm lines with the extracted lines or points, causing

huge information loss. Thus, in the last decade, less researchers

re concerned about it.

For the ridges-centered extraction method, Huang et al.

86] proposed a novel ridge feature extraction method based on

ts orientation and frequency. They used a bank of Gabor filters

o capture both local and global details for representing ridges as

D. Zhong et al. / Neurocomputing 328 (2019) 16–28 21

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ifferent point sets. Corresponding equal error rate (EER) was as

ow as 1.5%. Others valuable works worth referring to are listed

n [87,88] . It merits attention that ridge-based methods are usu-

lly applied in high-resolution acquisition system that is applicable

or privacy protection. Nevertheless, extravagant demand of photo

uality and low processing speed hinder its widespread usage.

For the line and points-centered way, Li et al. [89] first reduced

he noise in the image. Then, palm-lines were detected based

n diversity and contrast. They then improved Hilditch algorithm

nd applied an edge tracking approach to get rid of branches.

inally, single pixel principal palm-line image was obtained after

onnecting the broken lines. With respect to point-based meth-

ds, three related feature extraction techniques: Scale Invariant

eature Transform (SIFT), Harris corner detector, and Histogram of

radient (HOG) in combination to Gabor filter are tested [90] for

ontact-based and contactless palmprint identification. However,

revious line-based approaches focused only on the direction of

alleys, Kim et al. [84] extracted slope direction of local plains

s well as the direction of valleys for matching. Specially, we

ighly recommend [91] as an exemplary research, because a new

eature entering space and an LBP-like descriptor called local line

irectional patterns (LLDP) that works in the local line-geometry

pace were proposed. Then, explicit comparison was provided to

how the robustness and significance of palm lines in recognition.

owever, this paper only considered the direction with the lowest

esponse of the descriptor though other directions of the palm

ines may contain useful information. Other creative works are as

ollowing [92,93] . Among them, the latest overview on edge de-

ection algorithms is available for reference. It investigated several

opular methods that were major determinants of the recognition

roperty, i.e. Sobel, Prewitt, Roberts, Laplacian of Gaussian (LOG),

nd Canny [93] .

3) Statistics-based methods

This type of methods is initially proposed out of statistical con-

eption of an image, i.e. variance, standard deviation, mean value,

nvariant moments, density. From the articles we reviewed, there

xist two research directions, one is transform-based and the other

s none-transform based method.

Classical transforms are comprised of wavelet transform,

ourier transform. They can perfectly represent the multiscale in-

ormation of a palmprint image in frequency domain. However, all

f the extraction work should be done on a small scale in the

ircular, rectangular or elliptical form. That means this research

rientation is local-based. In recent decade, more comprehensive

ransforms were proposed, such as discrete curvelet transform [94] ,

iesz transforms [68] , Force field transformation [48] and digital

hearlet transform [95] . After the transform, statistical indicators

ere computed and converted to a vector for matching. A novel

pproach [32] improved Local Binary Pattern Histogram (LBPH) and

ombined it with Dual-Tree Complex Wavelet Transform (DT-CWT).

he final representation of feature was weighted histogram set.

None-transform statistical methods generally originated from

he study of Zernike moments. However, the order or dimen-

ion of moments is low which cannot incorporate adequate in-

ormation. Therefore, Gayathri et al. [96] devised a robust iden-

ification system using high order Zernike moment. The method

s hardly influenced by rotation, and occlusion because of its or-

hogonality and rotation invariance characteristics. New moments

resented are as following [97,98] . Besides, there are also recog-

ition schemes fusing transforms with invariant moments to in-

rease images’ quality [99,100] . In consideration of statistical ob-

ects like image center of gravity and density, we have found few

orks. Only in 2011, Dai et al. [25] confirmed the discriminative

ower of density after plenty of experiments on high-resolution

almprint recognition. Primary reason may be the overlooking of

ome discriminative structural palmprint information in the holis-

ic none-transform based methods.

4) Subspace methods

Subspace methods also stem from face recognition. Commonly

sed subspace methods regard palmprint image as high dimen-

ional matrix or vector and convert it into a low dimensional one

y projection or mathematical transform. Subsequent representa-

ion and classification were then utilized for valid image matching.

n general, different training sets of different types of palmprint

eed to be established, and the optimal projection vector or ma-

rix was chosen to represent the feature.

As we know, during the formation of a training set, every class

erived from it can be attached with label information. Traditional

ays like PCA, independent component analysis (ICA) and locality

reserving projection (LPP) did not use such information while

inear discriminant analysis (LDA) did. Afterwards, researchers

ombined PCA with LDA to consider both the discriminance and

epresentation of palmprint. However, the dimension of palmprint

mages is always larger than the number of training sets. Accord-

ngly, two-dimensional PCA (2DPCA), Bi-directional PCA (BDPCA)

nd its fusion with LDA were discussed aiming to solve small

ample size (SSS) problem. Meanwhile, many works have been

one on LPP, such as 2DLPP, two-dimensional discriminant LPP

2DDLPP), orthogonal discriminant LPP (ODLPP) that reduce noise

fficaciously. All the above approaches use linear projection or

athematical transform model. Popular nonlinear methods are

ernel-based, like kernel PCA (KPCA) with an underlying nonlinear

patial structure and kernel Fisher discriminant (KFD).

Recently, researchers have made huge progress in optimiza-

ion and application of PCA. For the sake of eliminating over-

tting and stepping up recognition, Bai et al. [101] designed a

ovel method by combining blocked surface type (ST) feature and

CA for 3D palmprint identification. They adopted histogram of

locked ST as palmprint feature so the computational complexity

as reduced. Researchers have also applied PCA to classify square

almprint overlapping blocks into either a good block or a non-

almprint block [47] . Also, PCA was fused with other extraction

ethods like moment invariances [97] to receive a comparatively

igh recognition rate. Other meritorious improvements are as fol-

ows: [102,103] .

About LDA, researchers have made breakthroughs as well. The

mage-Based LDA (IBLDA) was put forward [104] to complete mul-

ispectral fusion. With post-processing approaches appearing, it

as extended to palmprint recognition [70] using two databases to

valuate post-processed LDA method. Experimental results proved

reat effectiveness. In addition, other advances like two dimen-

ional LDA (2DLDA), Gabor-based two directional two dimensional

DA (GB (2D) 2 LDA) are presented in [105,106] .

Taking ICA into account, winner-take-all based ICA (WTA-ICA)

107] as a form of sparse ICA, is simpler to meet with high di-

ensional computing needs. Chen et al. [81] even combined back

ropagation neural network with ICA and transformed original ROI

mages into a small set of feature space.

About the improvements in LPP, Pan et al. [29] improved 2DLPP

ased on the Gabor features (I2DLPPG) to enhance the accuracy

ate and reduce calculation and storage complexity. Another modi-

cation work [108] created diagonal Dia-DLPP (DLPP) and weighted

wo-dimensional DLPP (W2D-DLPP). They assigned a weight to

ach pixel for manifold learning about palmprint. Moreover, a

ovel approach called Fisher Locality Preserving Projections (FLPP)

59] was presented for efficient recognition. Besides, Lu et al.

109] also considered fusing LPP and machine learning methods.

As the most popular nonlinear method, Kernel based meth-

ds represent image data into a higher dimensional or bound-

ess dimensional feature space. Except KPCA, KLPP [110] , KFD, KDA

22 D. Zhong et al. / Neurocomputing 328 (2019) 16–28

Table 3

Summary of the kinds of feature and ways to record feature. Feature column expresses the kinds of feature extracted and

encoding/photo/learning column expresses the ways to represent features.

Feature Encoding/Photo/Learning Year Article

2D PCA & 2D LPP photo 2008 [29]

Energy & frequency

“Laplacianpalm” feature

learning

photo

2008

2008

[80]

[4]

Between-class relevant structures photo 2008 [69]

LRV(Local relative variance) encoding 2009 [38]

Shape & texture encoding 2009 [32]

Orientation encoding 2009 [45]

MCI & GCI & ST(Mean & Gaussian curvature image & surface type) encoding 2009 [21]

2D PCA photo 2010 [70]

ICA

Zernike moment

Discrimination

learning

learning

photo

2010

2010

2010

[77]

[76]

[58]

Texture encoding 2011 [44]

Mean & standard deviation photo 2011 [28]

GMP (Gabor magnitude and phase) information photo 2011 [62]

Palm line photo 2011 [115]

Orientation encoding 2011 [26]

Texture encoding 2012 [39]

PCA & DWT (Discrete wavelet transform) in quaternion model photo 2012 [30]

Local phase & orientation encoding 2012 [68]

Texture encoding 2012 [53]

2D&3D PCA photo 2013 [116]

Texture encoding 2013 [48]

Phase

Orientation & texture

learning

encoding

2013

2013

[60]

[35]

Frequency & Orientation photo 2013 [60]

Texture photo 2014 [49]

Texture photo 2014 [41]

Texture encoding 2014 [46]

Orientation photo 2014 [50]

Orientation photo 2014 [57]

Texture encoding 2015 [117]

Texture

Class label information

learning

photo

2015

2015

[72]

[118]

Blur invariant phase encoding 2015 [43]

Location & Discrimination photo 2015 [64]

Orientation encoding 2015 [54]

Orientation encoding 2015 [52]

Discrimination photo 2015 [59]

Texture photo 2016 [119]

Texture encoding 2016 [51]

Energy information photo 2016 [63]

Orientation & Line information encoding 2016 [65]

Double-orientation encoding 2016 [61]

Direction & Line photo 2016 [14]

Orientation encoding 2016 [55]

Texture encoding 2016 [120]

Orientation photo 2016 [56]

ROI

Lines

PCA

Concavity information

learning

learning

learning

encoding

2016

2016

2016

2017

[74]

[73]

[71]

[85]

Texture & orientation encoding 2017 [67]

Local orientation encoding 2017 [16]

Frequency & Orientation photo 2017 [12]

Discrimination photo 2017 [24]

Texture encoding 2017 [121]

Direction & Line photo 2017 [66]

Discrimination photo 2017 [15]

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[51] , kernel PCA-subclass discriminant analysis (KPCA-SDA) [23] ,

our first suggestion is [111] which put forward kernel trick based

sparse representation (KSR) algorithm. The algorithm decreased

the feature quantization inaccuracy when the sparse coding effec-

tiveness was improved simultaneously.

The final progress is the combination of image transform and

subspace techniques.

Image transform has many advantages related to noise elimi-

nation and enhancement of robustness and efficiency. Algorithms

with the transforms applied before feature extraction process on

PCA [112] , LDA [113] , ICA [114] are definite to perform better com-

paratively.

5) Machine learning and deep learning methods

So far, palmprint recognition technologies in machine learn-

ng and deep learning [60,71,72] have thrived on a large scale.

raditional machine learning can be divided into two categories,

upervised, like convolutional neural network (CNN) and non-

upervised learning, like deep belief network (DBN). Three crucial

oints in a particular learning system are activation function, loss

unction and optimization strategy. A typical deep learning system

enerally consists of diverse layers, for example, CNN is comprised

f input layer, convolutional layer, fully connected layer, pooling

ayer, softmax layer and output layer.

D. Zhong et al. / Neurocomputing 328 (2019) 16–28 23

Table 4

Summary of distance calculated for matching. Distance expresses the kinds of distance calculated for

matching comparison.

Distance Year Article

Euclidean distance 2008 [122]

Manhattan distance 2009 [38]

Chi-square distance 2009 [32]

Zhang et al. 2009 [21]

Gui et al. 2010 [126]

Hamming distance 2011 [44]

Euclidean distance 2011 [124]

Angular distance & orientation equivalence 2011 [115]

Hamming distance 2011 [26]

Euclidean distance 2012 [123]

Euclidean distance 2012 [30]

Hamming distance & FPD(Fragile bit pattern distance) 2012 [53]

Hamming distance 2012 [47]

CW-SSIM distance 2012 [19]

Hamming distance & Angular distance & Pixel-to-area distance 2012 [27]

Euclidean distance 2013 [127]

Euclidean distance 2013 [128]

2D-Euclidean distance & Angular distance 2013 [35]

Cosine & Euclidean distance 2013 [129]

Reconstruction error & Normalized distance 2013 [125]

Raghavendra et al. 2014 [49]

Wen et al. 2014 [130]

Wang et al. 2014 [63]

Xu et al. 2014 [131]

Cui et al. 2014 [22]

Chi-square distance 2014 [46]

Raghavendra et al. 2015 [117]

Chi-square distance 2015 [43]

Hamming distance 2015 [54]

Deviation 2015 [132]

Hamming Distance 2015 [52]

Xu et al. 2016 [95]

Hamming distance & Kullback–Leibler divergence 2016 [65]

Nonlinear angular 2016 [61]

Principal line distance 2016 [14]

Normalized correlation coefficient (NCC) 2016 [56]

Angular Hamming distance 2017 [85]

Modified angular distance 2017 [133]

Cosine Mahalanobis distance 2017 [24]

Peak-to-sidelobe ratio 2017 [66]

Table 5

Summary of fusion in objective and level. The objective expresses the information used for fusion and level

shows the level of fusion.

Objective Level Year Article

Palmprint & palm vein Pixel 2008 [4]

2D & 3D Feature & score 2009 [21]

Palmprint & FKP(Finger knuckle pattern) Score 2011 [44]

Multispectral Score 2011 [124]

Minutiae & Density & Orientation & Principal lines Feature 2011 [25]

Palmprint & Face Score & pixel 2011 [108]

2D &3D Score & feature 2013 [116]

Multispectral & 2D & 3D images Feature 2013 [128]

LBP & Gabor wavelet feature Score 2013 [35]

Comp code & TPTSR Score 2013 [31]

Multispectral Score 2013 [134]

Multispectral images Pixel & score 2014 [49]

Interdigital & thenar & hypothenar Score 2014 [63]

2D & 3D Score 2014 [22]

MC & GC(Mean & Gaussian curvature) Decision 2015 [34]

FKP & palmprint Score 2015 [135]

Multispectral Pixel & feature 2015 [64]

Multispectral Feature 2015 [52]

Multispectral images Pixel 2016 [95]

Multispectral Feature 2016 [65]

2D & 3D Score 2017 [24]

Multispectral Score 2017 [136]

24 D. Zhong et al. / Neurocomputing 328 (2019) 16–28

Table 6

Comparative study of palmprint recognition methods. Database size refers to the number of images used for recognition. Time refers to the

average time cost in identification process. Feature size refers to the template size used for matching.

Method Indicators Feature size Database size Time Article

CR_CompCode (encoding) 98.78%(rank 1 RR) 256 ∗256 12,0 0 0 12.5 ms [16]

AF coding (encoding) 97.2%(RR) 128 ∗128 800 35 ms [67]

LLDP (structure) 100%(rank 1 RR) 128 ∗128 4600/5021 23.42 ms [91]

Ridge Distance (structure) 0.29%(EER) 2040 ∗2040 1280 89 ms [88]

2D-DOST (statistics) 97.29%(RR) 128 ∗128 7752/5502 341 ms [114]

High order moments (statistics) 99.59%(RR) 128 2 /192 2 549/5239/7752 202/172/234 ms [137]

ST & PCA (subspace) 99.25%(RR) \ 80 0 0 35 ms [138]

LPDP (subspace) 99.7%(RR) 32 2 /128 2 165/40 0/60 0 214 ms [139]

Multispectral fusion (fusion) 99.93%(RR) 128 ∗128 12,0 0 0 749.9 ms [136]

Face & palmprint (fusion) 99.17%(RR) 50 ∗50 1175 213 ms [140]

CNN for ROI (learning) 99.59%(RR) 196 ∗147 5239 31.77 ms [74]

DCFSH (learning) 0.0 0 0%(EER) 128 ∗128 7752 \ [141]

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Many researchers applied machine learning [75–81] either for

feature extraction or for classification. In the latest 3 years, because

of deep understanding of the artificial neural networks, Zhao et al.

[72] proposed an overview of deep learning in palmprint recogni-

tion. Liu et al. [73] used CNN to carry out contactless recognition.

And also a novel preprocessing measure is presented [74] based on

CNN. The average accuracy of deep learning is much higher than

that of classic approaches, even reaching 100%. Thus, it is a very

promising field.

In conclusion of feature extraction, Table 3 represents the sum-

mary of the types of feature and ways to record it. As shown in

table, both fusion method and deep learning have become the pop-

ular tendency of feature extraction. Similarly, a hypothesis can be

made that the ideal way to represent feature may be the one that

fuses encoding and photo ways under the deep learning structure

in the near future.

3.2. Matching

Matching is the final step of palmprint recognition, and it is

the most essential step. The purpose of matching is to figure out

the testing palmprint image belongs to which class in the dataset.

Whether the feature is matched properly will influence the effect

of recognition system.

In this survey, how algorithms are conducted is not explained

but the matcher for comparison is mainly discussed. For differ-

ent image databases, different distances calculated will lead to dif-

ferent discrimination between the same people. Recently, many

traditional distances were still applied, such as Euclidean dis-

tance [30,122–124] , Hamming distance [26,44,47,52–54] and Chi-

square distance [32,43,46] . Some new distances were developed

as well, for example, Angular distance [115] , CW-SSIM (Complex

wavelet-structural similarity) distance [19] , Peak-to-sidelobe ratio

(PSR) [66] and Cosine Mahalanobis distance [24] are well stud-

ied. Multi-distance was also a new phenomenon. They usually

used weighted sum of multiple matchers to calculate the differ-

ence [27,35,65,115,125] . Table 4 shows the summary of distance

calculated for matching. More novel types of distance and multi-

distance newly emerged.

4. Fusion

Fusion of biometrics is the tendency with the development of

information fusion techniques. It has mutual-complement advan-

tages and can overcome drawbacks of unimodal biometrics. Several

fusion rules nowadays include minimum, maximum, sum, average,

SVM and neural networks. Fusion related to palmprint consists of

the fusion of objectives and fusion of methods for extraction and

matching.

Considering objectives used for fusion, there are many cate-

gories, such as different biometric information [4,44,108] , differ-

nt images types [21,49,128] and different features [25,31,35] . In

ddition, the level of fusion also differs in corresponding recogni-

ion system. Chief levels can be divided into four categories, pixel

4,64,95,108] , feature [21,25,52,65,128] , score [31,35,44,124,134] and

ecision level [34] . Some articles even applied two levels to

trengthen the recognition [21,49,64,108,116] . Table 5 shows the

ummary of fusion in terms of objective and level.

. Selected comparative experiments

In order to validate the performance of various methods, the

atest experimental results from the selected state-of-art works

ere presented for comparative study. It is worth noting that the

esults of them cannot be compared directly because of different

ardware condition and experimental setup.

Table 6 summarizes comparative experiments and their results.

he result indicators are RR (recognition rate) and EER. Moreover,

e choose the best experimental result in every research. Some

bbreviations used below are as follows: Anisotropic Filter (AF)

oding, LLDP (Local line directional pattern), 2D-DOST (2D discrete

rthonormal S-Transform), ST (Blocked surface type feature), LPDP

Locality preserving discriminant projections), and DCFSH (deep

onvolutional features based supervised hashing).

According to our survey, almost all the noticeable characteristics

re already considered during these years, such as shape, texture,

requency, direction, energy and phase information of a palmprint

mage. Except for innovations about the recognition or detection

tself, plenty of attention is focused on the optimization of exist-

ng strategies, thus the recognition accuracy increases like what is

emonstrated in the chart.

Essentially speaking, deep learning, subspace and encoding

ethods have superior recognition precision. Genetic features with

xcellent generalization capability can be derived by learning

ethods from large datasets, which are conveniently transplanted

nto palmprint recognition. Encoding methods did not differenti-

te which kind of feature is in the image but encode it under the

ame guideline so little feature is lost. Subspace methods train im-

ges highlighting on the features’ pattern and then obtain the pro-

ection matrix from them. Besides, deep learning approaches have

oth the biggest computational complexity and the biggest feature

ize. The reason is that too much data are needed for training so

ubstantial computing work is unavoidable. Usually, GPU is highly

equired. Similarly, structure-based methods focus only on the di-

ection and location of palm lines rather than representation of

eatures, thus the accuracy, spatial and computing complexity are

ot very ideal for extensive application. Finally, fusion approaches

olves several problems in unimodal biometrics, i.e. noisy data, il-

umination variation, partial occlusion and non-universality that

ause the system to be less accurate and secure. However, the ef-

ciency decreased meanwhile compared to other methods because

f extra work done for another fusion objective.

D. Zhong et al. / Neurocomputing 328 (2019) 16–28 25

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. Conclusion and discussion

.1. Conclusion of this survey

Palmprint recognition is a promising method for identity au-

hentication with the superiority of safety and stableness. In recent

ecade, it developed fast and meanwhile had many breakthroughs.

n this survey, the basic knowledge of palmprint recognition was

ntroduced firstly. Then, the developments of image acquisition

nd preprocessing were presented using a tabulation. Many new

atabases were established for simulating real world better and

ovel ways to segment ROI were also proposed. We surveyed

lmost all the valuable methods for the preprocessing stage. In

he third part, feature extraction and matching were concerned.

ifferent f eatures were obtained using three general types of

xtraction algorithms. Next, palmprint matching measures were

xplained. In the fourth part, our paper discussed the tendency

f fusion. A fact can be found that fusion improves the accuracy

f recognition and becomes increasingly popular. Finally, some

elected experiments are presented and analyzed to conduct a

omparative study as well.

.2. Suggestions on further research

After reviewing the recent works, we would like to provide our

ve suggestions and some burning research issues for further in-

ovation.

The first one should be taken into consideration is the practical

pplication. As we know, though palmprint is under research for

onsiderable years, still there are not many genuine examples

o make full use of its recognition in practice. Firstly, Rotation,

ranslation, blurring, distortion and heterogeneous data of the

equired images still resists further development [56,142] . Sec-

ndly, researchers need to figure out how to design an appropriate

lgorithm when images are captured under low or high contrast

onditions or by a contactless way. Meanwhile, image quality

ssessment is also promising to reduce the high error rate caused

y poor quality of the testing images rather than the algorithm

tself. Besides, corresponding practical datasets containing all types

f palmprint images are expected to be established, i.e. low and

igh-resolution images, 2D and 3D images and multispectral im-

ges, which will serve as a benchmark. It is better to have all the

and features included, such as vein configuration and fingerprint,

o prepare for fusion or learning method. Thirdly, due to the

rogress of Internet [12,42] , more emphasis should be placed on

nline palmprint recognition and its use in mobile devices, which

ill become a novel identification approach in online payment or

ersonal authentication.

The second promising direction is the usage of deep learning.

oo many training samples are required and it also has little gen-

ralization ability. Recently, George et al. [143] proposed a prob-

bilistic generative model called recursive cortical network (RCN)

o conduct message-passing based inference. The method unifies

ecognition, segmentation and reasoning. Excellent generalization

nd occlusion-reasoning capabilities were demonstrated. Experi-

ental results are even better than deep neural networks while

he algorithm is 300-fold more data efficient. Thus, it is a field re-

earchers may focus more on.

The third research orientation is encoding based methods. First,

ncoding way has many advantages over photo ways and plenty of

esearches have been done perfectly [54,61] . Compared to the prin-

iple of ID cards, maybe through palmprint recognition, a vector

f bitwise code called palmprint ID will be attached to everyone’s

almprint, which will be used for forensics and security protection.

The next direction is fusion. It can be used in data acquisition,

reprocessing, feature extraction and matching, leading to a bet-

er recognition performance. However, the objectives applied in fu-

ion is no more than three, maybe there should be more objectives

nvolved while total time expense shouldn’t be too high. Besides,

ometimes large amounts of information are neglected in fusion,

ausing a limited recognition rate. Finally, further researches are

xpected to consider the robustness of fusion to reduce impacts of

onstraints like illumination variation and condition changes.

The final key in palmprint recognition is liveness detection aim-

ng for high security capability. Though the palmprint cannot be

ost, forgery and duplication problems still exert huge bad influ-

nces on the recognition system. Liveness detection as a method

o detect human vital signs can prevent such attacks. Recent stud-

es like multispectral recognition [95] may be a possible solution.

Due to the limit of our perspectives, the above-mentioned sug-

estions on further research are for reference only. We welcome

eaders’ comments and suggestions.

cknowledgments

This work is supported by Grants from National Natural Science

oundation of China (No. 61105021 ), Natural Science Foundation

f Shaanxi, China (No. 2015JQ6257) and the Fundamental Research

unds for the Central Universities.

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aag2612 .

Dexing Zhong received his Bs.Sc. and Ph.D. degrees fromXi’an Jiaotong University in 2005 and 2010, respectively.

He is an associate professor in School of Electronic andInformation Engineering, Xi’an Jiaotong University, China.

He was a visiting scholar with University of Illinois atUrbana-Champaign, United States. His main research in-

terests are biometrics and computer vision.

28 D. Zhong et al. / Neurocomputing 328 (2019) 16–28

Xuefeng Du is an undergraduate student in School

of Electronic and Information Engineering, Xi’an Jiao-tong University. He is participating in the Information-

technology Talent Program (ITP) sponsored by school.

Kuncai Zhong is an undergraduate student of Xi’an Jiao-

tong University. He will be enrolled in Shanghai JiaotongUniversity as a graduate student in the fall of 2018. He is

participating in the national university student innovation

project.