Human Re-identification using Soft Biometrics in Video Surveillance

Human Re-identification using Soft Biometrics in Video Surveillance

Ph.D. Dissertation

Student: Shengzhe Li (이성철)

Supervisor: Prof. Hale Kim

May 22, 2015

1

Table of contents

• Introduction to soft biometrics

• Scene geometry and human height estimation• Simplified camera calibration

• Simplified camera calibration with distortion correction

• Human descriptors and re-identification• Combining motion and tracking-by-detection

• Automatic height estimation

• Feature-level color normalization

• Re-identification using soft biometrics

• Conclusion

• Achievement

2

Introduction to Soft Biometrics

3

Biometric technology map

4

Comparison of various biometric technologies in a

perspective of sensing distance and accuracy

Recent research activities on Soft Biometrics

5

2014.11.30 JIVP, Special Issue on Soft Biometrics:

Extraction and Applications based on Images and Videos

2015.1.15 PRL, Special Issue on Soft Biometrics

2014.9.6 ECCV, First International

Workshop on Soft Biometrics

Definition

Encyclopedia of Biometrics“Any anatomical or behavioral characteristic that provides some information about the identity of a person, but does not provide sufficient evidence to precisely determine the identity can be referred to as a soft biometric trait. Personal attributes like gender, ethnicity, age, height, weight, eye color, scars, marks, tattoos, and voice accentare examples of soft biometric traits. ”

6

“a new form of biometric

identification which use physical or

behavioral traits that can be

naturally described by humans”

Daniel A. Reid Mark S. Nixon

Jean-Luc Dugelay

“the soft biometric traits instances are

created in a natural way, used by humans to

distinguish their peers.”

Antitza Dantcheva

State of the art technologies

7

Methods and performance

Domain of application

• Type I: Fusion with classical biometric traits

To integrate the information provided by soft biometric signatures with the ones of a primary biometric system.

Framework of integration of soft biometrics to improve the accuracy of classical

biometric systems [Dantcheva2010]8

State of the art performance (type I)

• Build a tunnel to collect soft labels are manually

• Performance of face recognition is increased by fusing soft labels

• Soft labels are more useful at far distance

9

[Tome2014]


• Type II: Pruning the search

The soft biometric signature is used as a side information to filter the original dataset W and to find a subset of the dataset Z.

Framework of integration of soft biometrics to improve the search efficiency of classical

biometric systems [Dantcheva2010]

10

intuVision

• Focuses on categorical classification of people from ordinary video

• face-based gender and ethnicity classification

• gender, ethnicity, age and size

11

http://www.intuvisiontech.com/

http://www.intuvisiontech.com/


• Type III: Human identification (re-identification)The identification approach is based on a signature composed by soft biometric traits, which can be extracted from images or videos

The scheme presents the

design of an identification

system based on soft

biometric traits.

[Dantcheva2010]

Very few studies

are performed in

this type

12

State of the art performance (type III)

13

[Dantcheva2010]

• Defines of two novel traits: weight and clothes

color

• Proposes a human identification based solely

on soft biometric traits

Collision probability of the proposed

BoSB in an N sized authentication

group with N ranging from 0 to 1,000

(a), and a magnified version in [0 100].

Soft Biometric Traits

14

Table of soft biometric traits [Tome2014]

Soft Biometric Traits

15

Table of soft biometric traits [Jaha2014]

Some commonly used soft biometric traits are

height, weight, age, ethnicity and gender, skin color, hair color and cloth color.

Two fundamental problems

• Scene geometry• Scene geometry (camera calibration) is

fundamental to obtain soft biometric features such as height and weight in video surveillance

• Camera calibration is very complicated and tedious because it needs the measurements in real-world

• Color consistency• Appearance of human changes within

one camera

• Color property changes very much between indoor and outdoor cameras

16

Camera calibration problem

Color inconsistency problem

Scope

• Study on theory and application of soft biometrics and person re-identification

• Propose a soft biometrics framework that can be accommodated in video surveillance

• Discover the illumination changes between indoor and outdoor under various weather condition

• Collect a real-world dataset for evaluating the system

Soft Biometrics

Person Re-identification

Database

Defines prototype and

framework

Data storage and

search

Feature extraction and

matching methods

17

Proposed method

• Part 1. Scene geometry and human height estimationMeasure the physical size of human in the image

• Simplified camera calibration

• Simplified camera calibration with distortion correction

• Part 2. Human descriptors and re-identificationExtract consistent features from human body

• Combining motion and tracking-by-detection

• Automatic height estimation

• Feature-level color normalization

• Re-identification using soft biometrics

18

Part 1. Scene geometry and human height estimation

19

Simplified camera calibration

Problems

Camera calibration experiments performed by Bin et al. at CVLab in 2011.

(Left) vanishing point based method. 8 points, height H, distance D1, D2.

(Right) DLT method. As many as possible points.

These method are accurate, but complicated and tedious!

It is natural to associate a walking or standing human with the camera

calibration problem in the context of video surveillance

20

Typical installation of camera

21

Indoor

Outdoor

Most cameras for video surveillance are

installed in high positions with a slightly tilted

angle to ensure the best field of view.

Main ideas

• The reason why camera calibration is complicated is that there are too much calibration parameters (five intrinsic and six extrinsic parameters).

• Reducing the number of calibration parameters can simplify the problem.

• Considering that most cameras for video surveillance are installed in high positions with a slightly tilted angle

• It is possible to retain only three calibration parameters in the original camera model, namely the focal length, tilting angle and camera height.

22

Coordinate system, notations

The typical camera installation and the

coordinate system in video surveillance.

23

Definition Notation

The world coordinates [X,Y,Z]T

The image coordinates [x,y]T

Head points in world [Xh,Yh,Zh]T

Head points in image [xh,yh]T

Foot points in world [Xf,Yf,Zf]T

Foot points in image [xf,yf]T

Focal length f

Tilt angle θ

Camera height c

Simplified Calibration

Most cameras for video surveillance are installed in high positions with a slightly tilted angle. In such installation, the rotation angles along axis Y and Z can be assumed as 0 (which are also known as pan and roll), as well as the translations along axis X and Z. Therefore,

• 𝑃 =𝑓 0 00 𝑓 00 0 1

1 0 00 cos 𝜃 − sin 𝜃0 sin 𝜃 cos 𝜃

1 0 00 1 00 0 1

0𝑐0

=𝑓 00 𝑓 cos 𝜃0 sin 𝜃

0 0−𝑓 sin 𝜃 𝑐𝑓 cos 𝜃cos 𝜃 𝑐 sin 𝜃

24


These three parameters can determine the mapping from the world coordinates[X,Y,Z]T to the image coordinates [x,y,w]T as

xyω

= 𝑃

XYZ1

=𝑓 00 𝑓 cos 𝜃0 sin 𝜃

0 0−𝑓 sin 𝜃 𝑐𝑓 cos 𝜃cos 𝜃 𝑐 sin 𝜃

XYZ1

=𝑓X

𝑓Y cos 𝜃 − 𝑓Z sin 𝜃 + 𝑐𝑓 cos 𝜃Y sin 𝜃 + Z cos 𝜃 + 𝑐 sin 𝜃

which can be represented in Cartesian coordinates as

xy =

𝑓X

Y sin 𝜃 + Z cos 𝜃 + 𝑐 sin 𝜃𝑓Y cos 𝜃 − 𝑓Z sin 𝜃 + 𝑐𝑓 cos 𝜃

Y sin 𝜃 + Z cos 𝜃 + 𝑐 sin 𝜃

25


A basic relationship between the world coordinates Y, Z and the image coordinates y, which is

given as

y =𝑓Y cos 𝜃 − 𝑓Z sin 𝜃 + 𝑐𝑓 cos 𝜃

Y sin 𝜃 + Z cos 𝜃 + 𝑐 sin 𝜃

=𝑓Y − 𝑓Z tan 𝜃 + 𝑐𝑓

Y tan 𝜃 + Z + 𝑐 tan 𝜃.

Since each pair of the head and foot of the y coordinates, denoted as yh and yf, can be

measured from the image. By above Eq., a set of equations with three unknowns can be built

as

yf =

−𝑓Z tan 𝜃+𝑐𝑓

Z+𝑐 tan 𝜃

yh =𝑓Yh−𝑓Z tan 𝜃+𝑐𝑓

Yh tan 𝜃+Z+𝑐 tan 𝜃

.

Eliminating Z,

yh =𝑓 𝑐tan2 𝜃+Yh+𝑐 yf+𝑓

2 tan 𝜃Yh

tan 𝜃Yhyf+𝑓(tan2 𝜃Yh+𝑐tan

2 𝜃+𝑐).

26

Foot y, real height

head y


The parameters can be found by the nonlinear regression as𝑓𝜃𝑐

= argmin𝑓,𝜃,𝑐

𝑖=1

𝑁

( yh𝑖 − yh𝑖)2 .

Once the calibration parameters of a camera are obtained, the physical height of a person can be estimated from a pair of head and foot points observed from the image.

Yh =𝑓𝑐 tan2 𝜃+1 (yf−yℎ)

tan 𝜃yhyf−𝑓yf+𝑓 tan2 𝜃yh−𝑓

2tan 𝜃.

27

Foot y, head y

real height

Parameter optimization

A scatter plot of the y coordinates of the observed and estimated head points with

respect to the observed foot points. The initial parameters f = 720, θ = -30 and c = -300

are approximated via visual estimation and the optimal parameters are found as f =

547.7, θ = -38.6 and c = -270.2 by the nonlinear regression method.

28

Optimal parameters can be found by the nonlinear regression.

Dataset for evaluation

• Number of subjects: 11

• Number of cameras: 9

• Video resolution: 1280 × 1024

• Location: Inha Univ. Hitech Bldg.

• Mark: Manual

29

Experimental results

30

Walking human based evaluation.

Ruler based evaluation

Comparison

Calibration

object

Mean

absolute error

Standard

deviation

Maximum

error

Krahnstoever Walking human 5.80% N/A N/A

Lee Cubix box or line N/A N/A 5.50%

Gallagher Grid pattern N/A 2.67cm 3.28cm

Jeges Grid pattern 2.03cm 4.17cm N/A

Proposed Walking human1.39cm

(0.80%)

1.91cm

(1.1%)

7.93cm

(4.5%)

Comparison of the proposed method with the existing height estimation

methods

31

Part 1. Scene geometry and human height estimation

32

Simplified camera calibration with distortion correction

Camera distortion problem

181cm169cm158cm

GT : 170cm33

Floor length estimation problem

GT : 396cm

AR : 322cm

GT : 271cm

AR : 278cm

34

The 4th order distortion model

• The types of distortion (from Wikipedia)

Barrel distortion

Pincushion

distortion

35

The 4th order distortion model

Camera distortion can usually be expressed as

𝑥𝑑 = 𝑥𝑢 1 + 𝑘𝑑1 ∙ 𝑟𝑢

2 + 𝑘𝑑2 ∙ 𝑟𝑢4

𝑦𝑑 = 𝑦𝑢 1 + 𝑘𝑑1 ∙ 𝑟𝑢2 + 𝑘𝑑2 ∙ 𝑟𝑢

4

where 𝑥𝑢, 𝑦𝑢 are undistorted(ideal) coordinates, 𝑥𝑑, 𝑦𝑑 are distorted

coordinates(real) and 𝑟𝑢 is the radius. 𝑘𝑑1 and 𝑘𝑑2 are the distortion

parameters.

The inverse of camera distortion model has same form but different

coefficients

𝑥𝑢 = 𝑥𝑑 1 + 𝑘𝑢1 ∙ 𝑟𝑑

2 + 𝑘𝑢2 ∙ 𝑟𝑑4

𝑦𝑢 = 𝑦𝑑 1 + 𝑘𝑢1 ∙ 𝑟𝑑2 + 𝑘𝑢2 ∙ 𝑟𝑑

4 .

36

Distort (original) image

37

Undistort image

38

Distort image

39

With distortion correction

AR: 170cm AR: 169cm AR: 174cm

GT : 170cm

40* AR : Algorithm Result, GT : Ground Truth

With distortion correction

AR : 357cm

GT : 396cm

AR : 278cm

GT : 271cm

41

* AR : Algorithm Result, GT : Ground Truth

Distortion correction results

42

-0.12

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

w/o distortion correction w/ distortion correction

Calibration error w/ and w/o distortion correction

Err

or

dis

trubution (

mete

r)

The calibration error with and without distortion correction. The

standard deviation is reduced from 3.73cm to 2.45cm after

applying distortion correction.

Conclusion

• The proposed method requires neither any special calibration object nor a special pattern on the ground, such as parallel or perpendicular lines; it does not rely on computing the vanishing points, which is difficult to estimate in practice

• The proposed method can be integrated with automated human detection methods to perform full autocalibration. This remains as a future study.

• Lens distortion correction offers more accurate height estimation especially for boarder area as well as the length in the floor.

43

Open source on Github (GPLv2 license)

https://github.com/lishengzhe/ccvs

44

https://github.com/lishengzhe/ccvs

Open source on Github (GPLv2 license)

45

42 visitors after two weeks

Open source on Matlab

46

40 downloads per month

Part 2. Human descriptors and re-identification

47

Human re-identification

Human re-identification consists in recognizing an individual in diverse locations over different non-overlapping camera views, considering a large set of candidates [Farenzena2010].

48

VIPeR dataset

Cam_A

Cam_B

3DPeS dataset

Approaches

• Three different re-identification approaches

• Global features

• 2D body models

• 3D body models

• Features• Global various types of color

histogram

• Local region and patch based descriptor, such as MSCR

• Combination of features• Feature level

• Score level

49

Three different re-identification

approaches [Balteri2014]

Main ideas

• Most re-identification methods only relay on image features such as color and texture

• Appearance of human might change within same camera because of wind or walking direction

• Color property changes extremely between indoor and outdoor cameras

• Re-identification using color and soft biometric trait can be measured in video surveillance such as height

• Tracking based approach for feature extraction

• Feature-level color normalization for constant color extraction

50Color inconsistency problem

Appearance of the human might change


51

Human detection and tracking

HOG on mini motion map

• Requirements• Processing time

• Stable bounding box

• Minimum false detection

• Proposed method• Compute motion map by

GMM and convert it to the mini motion map

• Compute HOG when mini motion map is none zero

Mini motion map for reducing

unnecessary computation in the

HOG based human detection.

Flowchart of the

proposed approach

combining motion

and tracking.

Method Time

Original HOG 60ms


(sparse)

20ms


(crowed)

30ms

Comparison of processing time

(i7 3.4G 4cores, @960 x 540)

Human detection and tracking (demo)

53


54

Human feature extraction

Automatic height estimation

• Camera calibration allow us to estimate height of human

• Steps for height estimation

Background subtraction

Rotate ROIProfile

histogram

Head foot points

extraction

Height computation

head

foot

[Lv2006]


For a 2D image the raw image moments 𝑀𝑖𝑗 with pixel intensities 𝐼(𝑥, 𝑦) are calculated by

𝑀𝑖𝑗 =

𝑥

𝑦

𝑥𝑖𝑦𝑖𝐼(𝑥, 𝑦)

The components of the centroid are

𝑥 =𝑀10

𝑀00

and

𝑦 =𝑀01

𝑀00.

Information about image orientation can be derived by first using the second order central moments to construct a covariance matrix.

𝜇20′ = 𝜇20/𝜇00 = 𝑀20/𝑀00 − 𝑥

2

𝜇02′ = 𝜇00/𝜇00 = 𝑀02/𝑀00 − 𝑦

2

𝜇11′ = 𝜇11/𝜇00 = 𝑀11/𝑀00 − 𝑥𝑦

http://en.wikipedia.org/wiki/Image_moment


The covariance matrix of the image 𝐼 𝑥, 𝑦 is now

cov 𝐼 𝑥, 𝑦 =𝜇20′ 𝜇11

′

𝜇11′ 𝜇02

′ .

The eigenvectors of this matrix correspond to the major and minor axes of the image intensity, so the orientation can thus be extracted from the angle of the eigenvector associated with the largest eigenvalue. It can be shown that this angle θ is given by the following formula:

θ =1

2tan−1

2𝜇11′

𝜇20′ − 𝜇02

′

The blob is rotated around centroid point (x, y) by angle θ.

http://en.wikipedia.org/wiki/Image_moment


58

Automatic height error

121110987654321

2.0

1.9

1.8

1.7

1.6

1.5

Subject

Height

Height extimation (auto)

Automatic height estimation error, slash lines represent GT.

Color feature

• Comparison of color spaces

10 20 30 40 50 60 700

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8Cumulative Matching Characteristic (CMC) Curves - VIPeR dataset

Matc

hes

Rank

HSV

Lab

RGB

HSV+Lab

HSV+RGB

Lab+RGB

HSV+Lab+RGB

HSV outperform the other color spaces

Combine HSV with the other color space degrade performance

Color property between cameras

Camera 01, outdoor, uniform light Camera 04, indoor, backlight

1 16 32 360

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

HSV histogram bins (16:16:4)

Mean f

eatu

re

Upper body HSV feature

0 16 32 360

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9


Mean f

eatu

re

Lower body HSV feature

0 16 32 360

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

HSV histogram bins (16:16:4)M

ean f

eatu

re

Upper body HSV feature

0 16 32 360

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9


Mean f

eatu

re

Lower body HSV feature

Comparison of mean HSV feature shows difference between cameras

Proposed method

• Feature-level color normalization• Assuming the means of color features are equal at same time

𝑓𝑚→𝑛(𝑥) = 𝑥 + ∆𝑚→𝑛

𝑛∈𝑁

𝑥𝑛 =

𝑚∈𝑀

𝑓𝑚→𝑛(𝑥𝑚) =

𝑚∈𝑀

𝑥𝑚 + ∆𝑚→𝑛

∆𝑚→𝑛=

𝑛∈𝑁

𝑥𝑛 −

𝑚∈𝑀

𝑥𝑚

• Advantages • No need pixel by pixel mapping

• Works for any color space

Experimental results

• Color

• Color + height

• Normalized color

• Normalized color + height

Dataset

Date of

recording

Length of

recordingWeather

Number of

cameras

Number of

subjectsVideo format

13/11/07 00:17:00 Cloud 5 7 1280×720

15/03/21 00:25:00 Sunny 7 12 1280×1024

15/04/04 00:23:00 Cloud 7 11 1280×1024

15/04/11 00:17:00 Sunny 7 15 1280×1024

13/11/07 15/03/28 15/04/04 15/04/11

Interactive performance evaluation tool for re-identification

GUI of the interactive performance evaluation tool for

re-identification

Features:

• Select dataset

• Select cam

• Select person

• Annotate GT

• CMC for cam

• CMC for all

Re-id result (131107, cam 1 2 4 5)

2 4 6 8 10 12 14 16 180

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1CMC Curve

Rank

Identification A

ccura

cy

Color

Color + Height

Re-identification result on dataset 131107 (cloudy)

13/11/07

67

5 10 15 20 25 30 35 40 45 50 550

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1CMC Curve

Rank

Identification A

ccura

cy

Color

Color + Height

Re-identification result on dataset 150328 (sunny)

15/03/28

Re-id result (150328, cam 1 2 4 5)

68

5 10 15 20 25 300

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1CMC Curve

Rank

Identification A

ccura

cy

Color

Color + Height15/04/11

Re-id result (150411, cam 1 2 4 5)

Re-identification result on dataset 150411 (sunny)

Re-id result (150404, cam 1 2 5 6)

5 10 15 20 25 300

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1CMC Curve

Rank

Identification A

ccura

cy

Color

Color + Height

Re-identification result on dataset 150404 (cloudy)

15/04/04

Re-id result (150404, cam 1 2 5 6)

5 10 15 20 25 300.4

0.5

0.6

0.7

0.8

0.9

1CMC Curve

Rank

Identification A

ccura

cy

Color

Color + Height

82.98 87.94

58.16 53.19

92.2 95.05

Re-identification result on dataset 150404

Re-id result (131107, cam04)

2 4 6 8 10 12 14 16 18 20 22 240

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1CMC Curve (cam04)

Rank

Identification A

ccura

cy

Color

Color+Height

N Color

N Color+Height


20 40 60 80 100 120 140 1600

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1CMC Curve (cam04)

Rank

Identification A

ccura

cy

Color

Color+Height

N Color

N Color+Height


20 40 60 80 100 120 140 160 1800

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1CMC Curve (cam04)

Rank

Identification A

ccura

cy

Color

Color+Height

N Color

N Color+Height

Conclusion and future works

• Conclusion• Simplified camera calibration using walking human is very practical

and results show promising results

• Proposed tracking based re-identification provide more rich information than image based ones

• Feature-level color normalization can balance color between cameras in an efficient way

• Re-identification performs better in a cloudy day

• Future works• More soft biometric traits can be added such as body profile, gait

frequency, gait speed

• Depth, 4K camera might provide more details about human and some soft biometric traits like hair color and skin color can be extracted

• On-line system is required to evaluate the proposed method in real-world application

75

Contributions of this study

• Simplified camera calibration• Simplified camera calibration significantly reduce time and effort

for camera calibration that has a benefit for many video surveillance applications

• Tracking based approach for re-identification• HOG on mini motion map provides two time faster processing time

while maintains same performance

• Tracking based feature extraction is more robust to appearance change in one camera

• Feature-level color normalization• Feature-level color normalization can improve the color

consistency between cameras

• Re-identification using soft biometrics• Combining color and height for person re-identification

improves existing method which mainly based on color only

76

Achievement

• Book chapter1. Hale Kim, Shengzhe Li, “Characterization and Measurement of

Difficulty for Fingerprint Databases for Technology Evaluation,” Encyclopedia of Biometrics, Springer US, 2014

• International journal1. X Cui, H Kim, S Li, KS Kwack, BH Min, Extraction of anatomical

landmarks and axis for 3D coordinate system construction of femur and tibia bone models, Osteoarthritis and Cartilage 23, A246-A248 (IF:4.663)

2. Shengzhe Li, Van Huan Nguyen, Mingjie Ma, Cheng-Bin Jin, Trung Dung Do and Hakil Kim, “A Simplified Camera Calibration Method for Human Height Estimation in Video Surveillance,” EURASIP Journal of Image and Video Processing (IF: 0.66, 2nd

review)

3. Thi Ly Vu, Trung Dung Do, Cheng-Bin Jin, Shengzhe Li, Van Huan Nguyen, Hakil Kim, and Chongho Lee, “Improvement of Accuracy for Human Action Recognition by Histogram of Changing Points and Average Speed Descriptors,” Journal of Computing Science and Engineering, Vol. 9, No. 1, March 2015, pp. 29-38

4. Shengzhe Li, Hakil. Kim, Changlong. Jin, S. Elliott, and Mingjie. Ma, "Assessing the level of difficulty of fingerprint datasets based on relative quality measures," Information Sciences, 268 (2014) 122–132. (IF: 3.89)

77

Achievement (continued)

• International journal (continued)5. Xuenan. Cui, Shengzhe. Li, Miao. Yu, Hakil. Kim, K.-S. Kwack, and B.-H. Min, "Automatic

cartilage segmentation and measurement for diagnosis of OA using 3D box and Gaussian filters," Osteoarthritis and Cartilage, vol. 21, pp. S232-S233, 2013. (IF: 4.663)

6. Naw Chit Too June, Xuenan Cui, Shengzhe Li, Hakil Kim and Kyu-Sung Kwack, "Fast and Accurate Rigid Registration of 3D CT Images by Combining Feature and Intensity.", Journal of Computing Science and Engineering, 2012, 6(1): 1-11.

• Domestic journals1. Jung-min Kim, Shengzhe Li, Hak-il Kim, “2D- 3D Human Face Verification System based on

Multiple RGB-D Camera using Head Pose Estimation”, Journal of The Korea Institute of Information Security & Cryptology, VOL.23, NO.6, Dec. 2013

2. Sheongwook Hong, Xuenan Cui, Shengzhe Li, Naw Chit Too June, Kyu-sung Kwak, Hakil Kim, “Efficient denoising and segmentation of multi-echo knee MR images,” Journal of the Korean institute of information scientists and engineers, software and application , Vol.38, No.68 pp. 340-346, June 2011

• International conference1. Shengzhe Li, Xuenan Cui, Miao Yu, Hakil Kim, Kyu-sung Kwack, "Detecting and visualizing

cartilage thickness without a shape model", SMC 2012: 232-236

2. Shengzhe Li; Changlong Jin; Hakil Kim; Elliott, S., "Assessing the Difficulty Level of Fingerprint Datasets Based on Relative Quality Measures", Hand-Based Biometrics (ICHB), 2011 International Conference on.

3. Changlong Jin; Shengzhe Li; Hakil Kim, "Type-Independent Pixel-Level Alignment Point Detection for Fingerprints", Hand-Based Biometrics (ICHB), 2011 International Conference on.

4. Shengzhe Li, Xuenan Cui, Naw Chit Too June, Hakil Kim, Kyu-sung Kwack: Label-guided snake for bone segmentation and its application on Medical Rapid Prototyping. SMC 2011: 752-757

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• International conference (continued)6. Seongwook Hong, Xuenan Cui, Shengzhe Li, Naw Chit Too June, Kyu-sung Kwack, Hakil Kim, “Noise removal for multi-echo MR images using global enhancement”. SMC 2010: 3616-3621

7. Changlong Jin, Shengzhe Li, Hakil Kim, Eunsoo Park, “Fingerprint Liveness Detection Based on Multiple Image Quality Features”, WISA 2010: 281-291

• Patent

1. 수술영상의실시간시각적노이즈자동제거장치, 방법및시스템, 2014.2, 등록

2. 복수의카메라들을이용한 3D 얼굴모델링장치, 시스템및방법, 2013. 9, 등록

3. ＧＰ－ＧＰＵ를이용한 3Ｄ의료영상정합의병렬처리방법, 2012. 8, 등록

4. 영상감시시스템을위한카메라감시영역산출장치및방법, 2014.7, 출원

5. 외부환경정보를반영한비디오보정시스템및방법, 2014.3, 출원

6. 무릎뼈의 3차원좌표시스템구축장치및방법, 2013. 8, 출원

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• Research project

• 지문인식

• 바이오인식데이터의보안및성능평가기술국제표준등록, 한국산업기술진흥원, 2009~2012

• 바이오인식시스템성능시험용 DB기준및구성방안마련, 한국인터넷진흥원, 2010~2011

• 제품기반의지문인식시스템성능및웹기반 BioAPI 표준적합성시험환경구축, 한국인터넷진흥원, 2012~2013

• 의료영상

• 골관절염의진단및치료반응평가를위한 MRI 및 CT 영상후처리알고리즘의개발, 아주대학교산학협력단, 2009~2014

• 지능형영상감시

• 스마트카메라를위한영상분석기술및사람인식성능평가방법/표준개발-원거리사람인식기술의성능평가방법및표준화, 한국전자통신연구원, 2011~2014

• 다수의고정형카메라기반특정보행자추적기술개발, 방송통신위원회, 2013~2015

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Human Re-identification using Soft Biometrics in Video Surveillance

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