Combined Local Features Selection for Face Recognition Based on Naive Bayesian Classification

Wael Ouarda 1,2, Hanene Trichili 1,2, Adel M. Alimi 1, Basel Solaiman 2

I Research Groups on Intelligent Machines, National School of Engineering of Sf ax, Tunisia 2 IT! Department Telecom Bretagne, Brest, France

wael.ouarda@ieee.org, hanene.trichili@telecom-bretagne.eu, adel.alimi@ieee.org, basel.solaiman@telecom-bretagne.eu

Abstract- Face recognition is a very popular biometric

solution in the literature. Several solutions are presented to

meet the needs of individual's verification or identification.

There are three types of face recognition approaches: local,

global and hybrid. In this paper, we proposed a local approach

for face recognition based on combined features selection

methods like Genetic algorithm, Gramdt Shmidt algorithm,

mRmR features selection algorithm and na'ive Bayesian

classifier. Our proposed approach will be compared with some

face recognition systems based on global features. A comparative study is given in this paper based on Recognition

rates and Execution times. Our Face recognition system, which

is based on na'ive Bayesian classifier and tested on ORL face

database, has showed 78.75% recognition rate and interesting

execution times compared to global approaches.

Keywords-component; Face recognition; Geaometric

Distances; Features selection ; lJayesian Classifier; Recognition

rates, Execution times

I. INTRODUCTION

Biometrics is a contribution arising from the evolution of human thought regarding secure access to forbidden places. Methods of biometrics differ from each other. Face recognition is an important technology which ranks top the list of existing in terms of cost and accuracy. Face recognition is based on four main modules: pretreatment, feature extraction, learning and classification. Several methods have been implemented in the detection and extraction of facial features. Below, we present a generic architecture of Face Biometric Systems.

Input Iml,e

User Identity

Figure I. Generic Architecture of Face recognition system

Pretreatment: it is used to improve the features of the 2D image of the person to be identified. It is very useful for face detection. There are several types of filters applied to enhance color, resolution and to minimize complexity of iIIwnination and background.

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Face Detection: face detection approaches can be categorized in four classes. There are Knowledge based methods which consist on use of such rules to define a face

region like rule 1 "eyes are symmetric", rule 2 " eyes area is darken than nose area", etc . The major drawback of these methods was the difficulty to defme a set of rules to allow detection system to localize faces. Second class of face detection approach is based on template matching. We define in this type of technique a function which defme a face template that can contains edge of each part of face like eyebrows, eyes, nose and mouth, etc. This approach is limited by occlusion and poses variation of face person. We can use also a function that defines faces templates like Eigen faces and fisher faces called by appearance based methods. The later class of method is based on feature invariant of face persons.

Features Extraction and Selection: the extraction and selection of features are two dependant steps for face recognition. A selection of relevant features always follows the preliminary extraction of features involved in each image representing a facial image. The Feature extraction is to represent an image of the data by a set of features to differentiate individuals.

Classification: the classification aims check the appropriate class of identity of input face image.

In the literature, there are three families of approaches of facial recognition: Global Approaches, Local Approaches and Hybrid Approaches. Soft Biometric is new research fields presented in recent works [1] [4]. The motivation of soft biometric use consist on the simplicity and the efficiency of soft biometric traits like shape characteristic of each parts of face, body and gait . . . All these features much be deduced from the classical facial recognition [3]. In this context, we demonstrate the efficiency of local approach based on geometrical distance. It can assist to face recognition with competitive performance like Global approaches. In order, it is simpler to extract soft biometric traits from local features then global ones. In fact, soft biometric is defined as the semantic description that we use to differentiate each other in

real life [4]. In our semantic description, we focused on local parts like skin color, shape of eyes, shape of head, shape of mouth, shape of chin, Age, Gender and ethnicity . . .

In this paper we present some experimental results done for face recognition based on global features. Then, we detail our proposed approach based on geometrical features. It combines three types of features selection methods and Nai"ve Bayesian Classifier. This approach aims later to integrate soft biometric traits to enhance system performance by applying Bayesian framework for fusion between two biometric systems: primary system developed in this paper and secondary system based on demographic soft biometric traits like gender and race. A comparative study is given to demonstrate several advantages of local approaches for face recognition.

II. RELATED WORKS

Face recognition systems are used in several applications especially with the progress of mobile application for face identification. This table can summarize some related work on Face recognition system tested only on ORL Face Database.

TABLE!. STAEE OF THE ART OF GLOBAL FEATURES BASED FACE

RECOGNITION

Ref Extraction Classification Recognition Execution

Rates Times (s)

[5] KPCA MahCosine

92.50% 113.770 Distance

[6] LDA MahCosine 86% 56.221 Distance

[7] PCA BPNN 90% 356.154

[8] PCA SVM 84.86% -

PCA + Multi Kernels

[9] Invariant for SVM 93% -Moments

[10] FLD+PCA RBF 93.75% 87.120

Self-[11] PCA Organization 74.6% 8.263

Map

All works shown In table I use dIfferent classIficatIOn methods based on global features with some techniques of space dimension reduction based on Principal Component Analysis and Linear Discriminate Analysis. The Recognition rates presented in table are very important. Although, we spent more times to evaluate performance of each system for two reasons: complexity of classifiers used like neural network, Support Vector Machines and global features extraction based on PCA and LDA. In this paper, we proposed a fast face recognition system based on NaIve Bayesian classifier and combined methods for local geometric distance selection.

III. FACE RECOGNITION APPORACH BASED ON COMBINED

FEATURES SELECTION AND NAIVE BAYESIAN

CLASSIFICATION

A. Preparing Data

ORL database contains: 40 people (male and female), 10 images per person. This database provides a wide variety of

poses and occlusions such as wearing glasses. Figure 2 presents some samples of face image organized by person:

•

i __

�

Figure 2. Some faces form ORL DataBases Annotation Points of Face image from ORL database:

We select 33 points which will be used for both Face recognition and classification of soft shape as the shape of the eyebrows eyes nose mouth and jaw biometric traits. The 33 points are categorized as follow.

Figure 3. Annotation Points in Faces from ORL Database To annotate ORL Faces database, we have imitate points

used for annotation of AR Face Database. It used almost the same points in face images.

Annotation Rules - As Face is a symmetric object, we begin always by the

left to Right parts - For Eyebrows, Eyes, we annotate faces with respecting

this process: - We begin by the head point and we finish by the of Jaw Figure 4 shows example of points annotated on face

image from ORL database:

Figure 4. Samples of face annotated

After applying Annotation of all 400 face images from ORL database, we can store all this points vectors for each image. Points Vector must be similar to figure 5.

2013 13th International Conference on Hybrid Intelligent Systems (HIS) 241

Points

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 2J 22 23 24 25 26 27 28 29 30 31 32 33

�� X y Designation

Head Point Left End of Left EyeBrows

High End ofLeit EyeBrO\\<"S Right End of Left EyeBro\\r"S Lower End of Left EyeBrows

Center of Left EyeBrows Left End of Rtght EyeBro",

High End of Right EveBrows Right End of Right EyeBrows Lower End ofRiRht EveBrows

Center of Right EyeBrows Left End of Left Eye High End of Left Eve Rtght End of Left Eye Lower End of Left Eye

Center of Left Eve Left End of Rt.ht Eve High End ofRtght Eye Right End of Right E 'e

Lower End ofRtght Eve Center of Right Eye

Left End of Nose High End of Nose Rillbt End of Nose Lower End of Nose Left End of Mouth High End of Nose

Rtght End of Nose Lower End of Nose

Center of Mouth Left End of J 3W

Rtght End of Jaw Lower End of Jaw

Points Labels

HP LELEB HELEB RELEB

LoELEB CLEB

LEREB HEREB REREB

LoEREB CREB LELE HELE RELE

LoELE CLE

LERE HERE RERE

LoERE CRE LEN HEN REN

LoEN LEM HEM REM

LoEM CM LEJ REI

LoEJ

FIgure 5. Features POInts annotated In Faces from ORL Database

Computing Distance setting based on Points of Face image

from ORL database. For setting, we defme 20 metric of Euclidian distance between 11 points selected for face recognition. The rest of points are used later into Facial soft Biometrics Traits classification for perspective work. Figure 6 summarizes all distances which will be later the input of selection features step to check most distance having higher variance applied to all face persons from ORL Database. For each image from the database, we define distance vector that will be used on our system as features vector.

Figure 6. 20 InItIal GeometrIc DIstances between POnIts annotated on ORL Faces

For each image we stored distance features vector. After that, more than one feature selection method is used to check the appropriate distance that can differentiate persons more. To qualify our annotation process for preparing data to selection and classification process, we have studied the distribution of each point annotated on each person from database enrolled with deviation in pose and variation in facial expression.

Figure 7. Distribution of points annotated in face images form ORL Database

B. Features Selection Step

Many feature selection methods have been used for the problem of classification. We note three families of feature selection methods: Wrappers, Filters and Embedded [16]. Wrappers method use training set as black box and selection task is based on predicted power. Filters method are based on preprocessing of training set by dividing based on pre selection of variables subset. Embedded methods are based on integrating features selection in training process. To ensure the robustness of face recognition system, we have used more than features selector combined. We used in this work Genetic algorithm [12] issued from the first family of features selection, Gram-Schmidt algorithm [13] from Embedded family and mRmR (Minimum Redundancy Maximum Relevance) features selection [14] which is very used in biomedical context and is issued from the third type of features selection.

Features selection based genetic algorithm. We used to select the pertinent features among the 20 distance taken before. We use more than features selection algorithm. Genetic Algorithm has shown in literature the most pertinence in features selection .

242 20i3 i3th international Conference on Hybrid intelligent Systems (HiS)

02

018

016

0.14

0.12

Fitness function 5=0 20051

generahon=11

Sl!:ll!:ctl!:d ..

,. '0

" 10 11

,

Figure 8. 7 distances selected from features vectors by Genetic Algorithm

Features selection based Gram-Schmidt algorithm. To assure the robustness of this step of features selection, we applied more than one selection algorithm. The next feature selection chosen is called Gram-Schmidt which is used in biomedical domain to select pertinent information to help in constructing the appropriate decision related to the patient.

;dx - gra�schm;d�(a. b. fea�num)

Tra;n;ng on fea�ure se� s;ze: 1 Tra;n;ng mse: 130. 70 Fea�ures selec�ed: ; dx-[20 ] Tra;n;ng on fea�ure se� s;ze: 2 Tra;n;ng mse: 123. 59 Fea�ures selec�ed: ; dx-[20 19 ] Tra;n;ng on fea�ure se� s;ze: 3 Tra;n;ng mse: 113. 92 Fea�ures selec�ed: ; dx-[20 19 18 ] Tra;n;ng on fea�ure se� s;ze: 4 Tra;n;ng mse: 110. 38 Fea�ures selec�ed: ;dx-[20 19 18 11 ] Tra;n;ng on fea�ure se� s;ze: 5 Tra;n;ng mse: 109. 1 8 Fea�ures selec�ed: ;dx-[20 19 18 11 3 ] Tra;n;ng on fea�ure se� s;ze: 6 Tra;n;ng mse: 104. 30 Fea�ures selec�ed: ;dx-[20 19 18 11 3 5 ] Tra;n;ng on fea�ure se� s;ze: 7 Tra;n;ng mse: 102. 30 Fea�ures selec�ed: ;dx - [ 20 19 18 11 3 5 10 ] ;dx -

20 19 18 11 10

Figure 9. 7 distances selected from features vectors by Gramdt Shmidt Algorithm

Features selection based mRmR algorithm. The third algorithm tested for features selection is known by MRmR Features selection algorithm. Figure 10 shows the output of this method of selection of pertinent distances among the 20 cited in foregoing.

140

120

40

coeficienls for featu,ltS gatl"ll'T\Ol � 5 beta 0

10 12 mdexoltealules

14

Figure 10. 7 distances selected from features vectors by mRmR algorithm

We present in table II the most relevant features selected given for each features selector.

TABLE II.

Feature Selection

Algorithms

Genetic Algorithm

mRmR Algorithm

Gramdt Shmidt Algorithm

COMPARISON OF OUTPUTS OF FEATURES SELECTION

ALGORITHM

10 Geometric Distances Selected

{O 18,D20,07,0 16,D1 O,DII ,D2,D3,04,D9}

{020,0 19,018,011,03,05,02,010,0 13,0 16}

{O I 0,D11 ,08,09,D18,D19,D5,D3,04,09}

Combining features selection. The combination of the three features selector aims to use distances which is taken at least by a one selectors. Our inputs for NaIve Bayesian classifier consists of these seven distances. {D20, D4, D12, D7, D18, D16, D2}.

IV. PARTITIONING DATA

For Data Partitioning, we used the same number of training set and testing set mentioned in works [5] and [6] from state of the art section.

30% of ORL Database for training 70% of ORL Database for testing (see figure 12)

Each person has ten face images. We use three image for training and seven face images for testing.

I�--�-�, I I I���� __ ��'��"�--����� � Figure II. Partioning data for Testing and Training data form ORL

Oatabase

V. CLASSIFICATION OF FACAIL FEATURES VECTORS

We used in this step, a face classification based on naIve Bayesian classifier which is based on Bayes theorem which says:

p(d)

2013 13th International Conference on Hybrid Intelligent Systems (HIS) 243

We note: • p(cjld) = probability of instance d being in class cj.

This is what we are trying to compute

• p(dlcj) = probability of generating instance d given class cj. We can imagine that being in class cj, causes you to have feature d with some probability

• p(cj)= probability of occurrence of class cj. This is just how frequent the class cj, is in our database

• p(d)= probability of instance d occurring. This can actually be ignored, since it is the same for all classes

We compared also our classification to some classifiers based on similarities distances: Euclidian and MahCosine Distances [5] [6] with modification of size of features vectors. Euclidian Distance

It's a metric given by the Pythagorean formula. In the Euclidean plane, if A = (xl, yl) and B = (x2, y2) then the distance is given by:

Euclidian_Distance (p,q) = ((PI - ql)2 + (P2 - q2)) 112

MahCosine Distance The MahCosine Distance is a metric to evaluate the

cosine of angle between two vectors after its transformation into Mahalabonis space. The MahCosine Distance between two points I and J with their projection a and b in the Mahalabonis space is given by:

MahCosine _Distance (iJ) = cos(8i.) = -------

lal*lbl

VI. COMPARATIVE RESULTS

We used in this paper a local features based on geometrical distances measured between parts of face person. All important distances with high variance between persons from ORL face Database are checked after combining three features selection methods. Our proposed approach shows from the comparative study presented in tables and figure below a good recognition rates 78.75% due to the use of naIve Bayesian classifier which has a interesting execution times compared to global approaches.

During testing, we used Intel Core i3 Processor 4 Go RAM and Matlab 2009b to apply different techniques of feature selection and classification.

TABLE III. NAlVE BA YES IAN CLASSIFIER PERFORMANCE V ARIA nON

WITH THE NUMBER OF FEATURES SELECTED

Features Geometric Face Recognition Execution Times

Distances Rates (Test) (s) (Learning)

20 distances 75,25% 1.205

7 distances selected 77.75% 1.401

10 distances selected 78.75% 1.225

Recognition Rates

80 �--------------------�

78 +----------...... �---

76 +-------�--���--�

74+-----��------�----�

72+-���----------���

70+---.-----------------�

68+---------------------�

66+-------------------�

64+-----�------�----�

20 10 7

geometric geometric geometric

distances distances distances

--+- Euclidian Distance

_MahCosine Distance

Naive Bayesian Classifier

Figure 12. Comparative study between developped classifiers based Distance similarity and Bayes rules

T ABLE IV. NAiVE BA YESIAN CLASSIFIER Vs EUCLIDIAN DISTANCE

Vs MAHCOSINE DISTANCE

Features Na'ive Bayesian Euclidian MahCosine

Geometric Classifier Distance Distance

Distances

20 distances 75,25% 70% 71,75%

7 distances 77.75% 78% 77.75%

10 distances 78.75% 72% 75%

TABLE V. COMPARSION BETWEEN OUR PROPOSED FACE

RECOGNITION APPROACH AND SOME WORKS FORM STATE OF THE ART DONE

ON ORL FACES DATABASE

Face Recognition Execution Times References

Rates (Test) (s)

[5] 92.50% 113.770

[6] 86% 56.221

[7] 90% 356.154

[8] 84.86% -

[9] 93% -

[10] 93.75% 87.120

[11] 74.6% 8.263

Proposed 78.75% 1.225

Approach

VII. CONCLUSION AND PERSPECTIVES

In the literature, there are three families of approaches to facial recognition: Global Approaches, Local Approaches and Hybrid Approaches.

In this paper, we studied local face recognition approach applied on ORL Database; we demonstrated that local approach for face recognition is faster than global one. Indeed, experimental study showed that all recognition rates are also similar. The main advantage of local face recognition approaches is that we can use the computing done to open to a novel trend and challenging which is called by Soft Biometric [15] to enhance face recognition rates. In

244 2013 13th International Conference on Hybrid Intelligent Systems (HIS)

next work, we aim to validate this comparison between local and global approaches by adding other experimental results of other classifiers like MLP Neural Network and Support Vector Machines.

For following work, we intend to fuse between the hard and soft biometrics in a Bayesian framework. The goal of information fusion based on Bayes rules is to proof that the soft biometrics traits which are deduced directly from the classical recognition, can improve the performance of facial biometric systems.

ACKNOWLEDGMENT

The authors would acknowledge the financial support of this work by grants from General Direction of Scientific Research (DRGST), Tunisia, under the ARUB program.

The authors would acknowledge the use of ORL Face Database.

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