Neural Networks in Face Recognition for

Juridical Communication Process

MĂDĂLINA COCOŞATUϖ, MIHNEA DRUMEAξ,

ALEXANDRU AVERIANα, SOFIA BRATUβ

ϖPublic Administration Department, S.N.S.P.A. ξLaw and Public Administration Department, Spiru Haret University

αMathematics Department, Spiru Haret University βJournalism, Communication and Public Relations Department, Spiru Haret University

Povernei 6, Sector 1, Bucharest ROMÂNIA

madacocosatu@yahoo.com, aaverian@yahoo.com, sofia_pub@yahoo.com http://www.snspa.ro

Abstract: - Automatic Juridical Communication Recognition (AJCR) is presented as a system for juridical communication recognition based on artificial neural networks (ANN). The whole system is implemented on a signal processor. Signal preprocessing is ensured, mainly by its Fourier transform, after which features obtained classifiers are applied to neuron architecture in corelation with subject and juridical juridicals.

Key-Words: - automatic juridical communication recognition, artificial neural network, juridical communication

1 Introduction Automatic recognition juridical communication in [4] is presented a system for automatic juridical communication recognition (AJCR - Automatic Juridical communication Recognition) based on artificial neural networks. The whole system is implemented on a signal processor. Signal preprocessing is ensured, mainly by its Fourier transform, after which features obtained classifiers are applied to neuron architecture (figure 1). Figure 1. Neuron arhitecture

The same problem is approached in a similar manner in [3].

2 Facial detection and recognition in juridical

process Architecture and implementation of an automatic system of face detection and recognition juridicals is a difficult one. An automatic detection and facial recognition in juridicals based on processing neurons requires following steps: a) image acquisition - used for taking pictures in the allegedly; in this case is used for human faces. For practical applications it is used product firm Creative Video Blaster WebCam 3 USB which enables images live in a color depth of 16.7 million, 30 fps at 352x288 resolution pixels or 15 fps at 640x480 pixels. b) image preprocessing - serves to improve the image quality taken from earlier stage and thereby increase overall performance (figure 2: 2a, 2b). Typical preprocessing techniques are used as histogram equalization in order improves contrast or filtration if it is contaminated with noise.

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Figure 2. The equalization of histogram growing up the quality of the image contrast Figure 2a. Original histogram and image

Figure 2b. Modified histogram and image

c) Face detection - requires spatial location of a facial image contained in stage. This affects quality of accuracy classification (recognition). This can be implemented both by classical methods and with ANN (artificial neural network). Surely, this involves processing a large volume of data, given the fact that the image be covered in all of a processing window of fixed size, at different levels of resolution (figure 3). Figure 3. Different levels of resolution

d) Extraction of features - by this stage extracts relevant information that characterizing a particular person. Also at this stage ensures a reduction of dimension input data applied after branch. There are many features extraction methods, both

conventional and neural. In the following, we will show conventional ways of extracting called method operator of interest. The procedure consists of extraction of various directional (horizontal, vertical and both diagonals) for each block of a facial image. These variables show local activity size of a block of the image. Regions with high variance successfully individualize the types present in the facial image. Equations for calculating the directional variance are:

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where µ is the average data and is calculated using the formula:

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and {p (y, x), 0 ≤ y ≤ P-1, 0 ≤ x ≤ Q-1} is the pixel intensity in a block P x Q. It will thus obtain for each block in a region of the image a set of five size:

LiiVHi ...1,},,,,{ 13545 == σσσσσθ ,

L being the total number of distinct blocks which is divided into a region of an image. Considering the original image of size N x M and P x Q window of which is extracted feature vector may deduct the value of reducing the dimension (RD) data applied after branch:

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Although this factor depends on the size of the window being extracted features for the specific case described (P = Q = 8) to obtain a discount of 12 fold volume of data to be processed by the neural classifier described in next paragraph, whichever is also the characteristic elements of a given image.

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e) Classification. Is the most important stage of the process of recognition facial and involves the allocation of a class (name, label) for each facial image features represented by the vector. It chose a solution neuronal to implement this step, based on architectures that use ANN-MLP (Multilayer Perceptron). The first two architectures using a single ANN-MLP, into it’s in the first case applies directly to the image pixel intensities and in the second case, features extracted by the method of operator interest in detail in paragraph previously. Figure 4a. The intensity of the pixels applied directly through ANN-MLP

Figure 4b. Features extracted through method of interest operator

The following two approaches use a parallel architecture of ANN-MLP whose input features extracted by the method applies operator in the region partially / totally bunk or unoverlapped (figure 5a, 5b). The difference between these latter two cases is how the decision-making regarding final classification of the facial image shown. In the figure 5a the answers of ANN -MLP are processed by an ANN-MLP superior hierarchically and in figure 5b classification is performed on ANN-MLP hierarchical mediation of responses below.

3 Experimental Results Experiments were performed on the database ORL (Olivetti Research Laboratory, currently owned by AT&T, United Kingdom) containing a set of facial images taken between April 1992 and April 1994 by ORL in Cambridge, UK [5]. Figure 5a. Final decision is taken by superior hierarchical ANN-MLP

Figure 5b. Final decision is taken by inferior hierarchical ANN-MLP

The database consists of 10 different images for 40 distinct subjects, so a total of 400 images. The images were taken in different time intervals in variation of lighting conditions, facial expression (eyes open / closed, smile, with / without glasses). All images contain a background homogeneous dark. Size image is 92x112 pixels, 256 levels of gray. In all cases in which no explicit otherwise, the number of image / person corresponding training phase is equal to 5 and so its image / person dedicated test phase will be all 5. Number of classes (persons distinct) is equal to 40, if not stated otherwise. It follows, therefore, a number of 200 images chosen at random at the beginning of each experiment, appropriate training phase and 200 random test images, the two sets being disjoint.

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Figure 6. The Database of faces at a glance

Source:http://www.cl.cam.ac.uk/research/dtg/attarchive/f

acesataglance.html

In table 1 are presented compared performances of two architectures figure 4 shown in a and b. In fact, using a single type ANN-MLP classifier, assessing where the image is applied directly without preprocessing, respectively if ANN-MLP entry applies a vector of features obtained from original image interest operator method. Training phase was considered terminated when the mean square error (MSE) reached value of 0.01%, which corresponds to a zero error on training data. 4 Conclusions The experimental results presented are averages table 1 calculated after making a total of 5 experiments. Average time of making experiments resulting from operations carried out by generating random order persons and related images that constitute the training dataset (40 persons x 5 images = 200 images), extracting features for the entire base data, ANN-MLP training, classification training dataset and test. Algorithms used to implement environment MATLAB v.5.3, 1999, PC hardware running on Intel Celeron 533 MHz. Examining the experimental results on the following:

• in using the system by extracting features are found to increase classifier performance by about 40% of the case withoutextraction of features; • there is applied to reduce the size of the data entry of ANN-MLP about 12 times when compared 4a to 4b; • average time / experiment is 4 times lower in the latter case, to contrast to the first case, the mean / experiment system extraction of features is not directly proportional to the number of patterns training. Previous findings us to consider the method of extraction features proposed as particularly effective, given the results obtained in similar conditions, the same database by other systems for recognition facial presented in literature.

References: [1] El-Bakry, H., Mastorakis, N., Fast Virus Detection by using High Speed Time Delay Neural Networks, Proceedings of the 10th WSEAS

International Conference on NEURAL NETWORKS, Prague, 23-25 March 2009 [2] Rasche, C., Douglas R.J., Forward and

Backpropagation in a Silicon Dendrite, IEEE Transition Neural Networks, pag.386-393, March, 2001 [3] Toderean, G., Coşteiu, M., Giurgiu, M., Reţele

neuronale, Ed. Microinformatica, Cluj-Napoca, pag.46, 1994 [4] Spilcă, A., Sisteme dedicate automate cu

comandă vocală realizate cu reţele neurale, Teză de doctorat, Universitatea Politehnica, Timişoara, 2000 [5]ftp://ftp.uk.research.att.com/pub/data/att_faces.tar.z AT&T database (ex Olivetti Research Laboratory), Cambridge, U.K., 2009

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Table 1. Average results for 5 consecutive experiments carried out with systems of figure 4a and 4b, ie a single neural classifier with / without extracting features of interest operator method. Original images were reeşantionate by a factor of ½ size is 56x46 pixels so and window size of features being extracted is 8x8 pixels.

1 img/pers 3 img/pers 5 img/pers MSE (%)

Total error (%)

Total error (%)

Total error (%)

Total error (%)

Total error (%)

Total error (%)

System Number of neurons input/hidden/output

Duration Experiment (min)

Duration Experiment (min)

Duration Experiment (min)

55,94 50,35 23,92 16,75 14,80 7,40 Figure 4a 2576 – 80 – 40 0.01 3,56 min. 10,14 min 16,66 min 41,16 37,05 14,50 10,15 8,80 4,40 Figure 4b 210 – 80 – 40 0.01 2,25 min. 3,27 min. 4,58 min.

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