HIGH PERFORMANCE POSE INVARIANT FACE

RECOGNITION

*Hasan Demirel and Gholamreza Anbarjafari

Department of Electrical and Electronic Engineering, Eastern Mediterranean University

Gazimağusa, KKTC, Mersin 10, Turkey

Keywords: Face recognition, Pose invariant, histogram matching.

Abstract: A novel pose invariant face recognition system based on grey

level histogram matching is proposed. The

proposed system in this paper uses grey level histograms as feature vectors for recognition of the different

poses of faces. The process is performed by taking the cross correlation between the histogram of a test face

and the histograms of the training faces in the database. The proposed system gives 98.80% recognition rate

on the HP database of 15 face subjects. This rate is down to 92% in the case of conventional eigenfaces

method.

1 INTRODUCTION

The advances in research and development of

multimedia systems have increased the use of

biometric authentication applications such as face

recognition. The earliest work in computer

recognition of faces is reported by Bledsoe (1964),

where manually located feature points are used to

recognize a faces. Kanade (1977) used a method of

characterizing the face by geometrical

parameterisation, whereby distances and angles

between points such as eye corners, mouth

extremities, nostrils, and chin top are used.

Statistical face recognition systems, such as,

principal component analysis (PCA) based

eigenfaces method introduced by Turk and Pentland

(1977) attracted a lot of attention by the researchers.

In this method the eigenvectors of the covariance

matrix of the training set of faces are used as the

basis vectors and any test image is mapped to the

representation vector in a low dimensional

eigenspace before classification. Relhumeur et. al

(1997) introduced fisherfaces method which is based

on the linear discriminant analysis (LDA) that

minimizes the discrimination within a class and

maximizes the dircrimination between classes. Their

class specific approach provided improved

recognition results and showed robustness to

illumination changes.

Another statistical descriptor can be considered

o be the histogram of a gray level image which

shows the distribution in terms of occurrence

frequencies of gray level pixel intensities. Histogram

of a face image can be considered as the signature of

the face, which can be used to represent the face

image in a low dimensional space. Images with

small changes in translation, rotation and

illumination still possess high correlation in their

corresponding grey-scale histograms. This histogram

characteristic prompts the idea of using grey-scale

histograms for face detection and recognition.

There is very limited work in histogram based

ethods in face recognition. Yoo et. Al (1999) used

chrom

atic histograms as a model of faces. They used

histograms for the detection of faces by

backprojecting a face histogram onto the entire

image containing a face to search and detect the face

in the backprojected image. Ojala et. al. divided a

face into several blocks and then the Local Binary

Pattern (LBP) feature histograms (2002) are

extracted from each block and concatenated into a

single global feature histogram which efficiently

represents the face image. Some other work have

cooperated with histogram to recognize faces (Chen,

2004, and David et. al, 2000). The recognition of the

face is performed by simple distance based

histogram matching.

In this paper, the gray level histogram of the entire

face im

age is used as the face descriptor of a given

face. The proposed face recognition system simply

uses gray level histograms to describe faces and

recognition is achieved by incorporating histogram

282

Demirel H. and Anbarjafari G. (2008).

HIGH PERFORMANCE POSE INVARIANT FACE RECOGNITION.

In Proceedings of the Third International Conference on Computer Vision Theory and Applications, pages 282-285

DOI: 10.5220/0001078402820285

 SciTePress

matching by using the cross correlation between the

histogram of the input face and the histograms of the

faces in the training set. The results obtained from

the proposed histogram based system provide a

recognition rate as high as 99.60% for the ORL face

database when 5 poses of each subject out of 40

persons are used to train and remaining 5 poses are

used for performance testing. The proposed method

clearly outperforms the classical face recognition

systems such as PCA based eigenfaces and LDA

based fisherfaces methods, where this rate is down

to 78% and 87% respectively.

Additionally, due to the high correlation between

the histograms of the faces at different scales, the

proposed system is robust for scale changes. The

recognition rate for smaller faces with sizes of only

30% of the images in the training set is 98.22%,

which is within 2% reduction in the recognition rate.

This result is expected, because the information loss

measured by entropy encountered by image

shrinking down to 30% of the original image is in

the range of 2%.

2 HISTOGRAM BASED FACE

RECOGNITION

One of the methods of describing an image in lower

dimension is using histogram. Histogram of an

image can be considered as feature vector

representing of the image. In general, histogram of

an image is a statistical description of the

distribution in terms of occurrence frequencies of

pixel intensities. The size of the image histogram

depends on the number of quantization levels of the

pixel intensities. Typical monochrome image with 8-

bit representation has 256 gray levels. In a

mathematical sense, an image histogram is simply a

mapping

i that counts the number of pixel

intensity levels that fall into various disjoint

intervals, known as bins. The bin size determines the

size of the histogram vector. In this paper the bin

size is assumed to be 256 and the size of the

histogram vector is 256. Histogram of a

monochrome image,

i, meets the following

conditions

∑

255

(1)

where N is the number of pixels in an image. Then,

histogram feature vector, H, is defined by,

],,,[

25510

"=H

(2)

The similarity between two images can be

measured by using the cross correlation between the

histograms of the respective images. The maximum

correlation coefficient in the correlation vector is

taken as the measure of similarity and used in the

histogram matching process.

If H1,H2,….,HM be a set of raining face images

with different poses and M be the number of image

samples, then a given query face image, the

histogram of the query image Hq can be used to

calculate the correlation between Hq and histograms

of the images in the training samples as follows:

max(),,

iiq

HH i M

==1D",

(3)

Thus, the similarity of the ith images in the

training set and the query face can be reflected

, the maximum cross correlation coefficient.

The, image with the highest similarity measure, is

declared to be the identified image in the set.

The proposed system using histogram as the face

feature vector and maximum cross correlation

coefficient as the histogram matching measure is

tested on Head Pose face database (Gourier, 2004),

which contains 15 subjects with 10 selected different

poses. The face dataset is divided into training set of

n (n≤5) images per subject and the rest images for

the test set. The images used in the test set are not

included in the training set. The correct recognition

rates in percent are included in Table 1. Each result

is the average of 500 runs, where we have randomly

shuffled the faces in each class. The results of the

proposed system are outstanding, because even a

single image in the training set provides a correct

recognition rate as high as 94.89%. This rate is down

to 68.89% in the PCA based face recognition

systems respectively.

The proposed method shows slight improvement

as the number of training set images is increased. On

the other hand PCA based systems reaches 92%. The

results are very encouraging and the proposed face

recognition system shows a clear superiority over

the conventional face recognition systems.

Table 1: Performance of the proposed histogram based

system compared with PCA based systems.

# of Training

Images

PCA

Proposed Histogram

Matching Method

1 68.89 94.89

2 81.67 94.62

3 89.52 98.14

4 92.22 97.33

5 92.00 98.80

POSE INVARIANT FACE RECOGNITION USING IMAGE HISTOGRAMS

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0 100 200

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4000

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0 100 200

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0 100 200

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0 100 200

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Figure 1: Faces with their PDF.

3 ROBUSTNESS ON POSE

VARIANCE

0 0.5 1 1.5 2 2.5 3 3. 5 4 4.5 5

100

Number of t raining set

Recognition Rate (%)

Hist ogram Matching

PCA

As fig. 1 shows pose differences (planer rotations)

on the images do not change the general distribution

of the grey level PDFs. The shape of the PDF is

more or less preserved, while only the amplitudes

are changing as the angle of rotation of the image is

changing.

So the shape of the histogram of a face image is

its signature in representing the image. Hence, if the

general distribution is preserved, then the correlation

of the histogram of the image with high and low

resolutions will be high.

Fig.2 shows the performance of the histogram

based face recognition system for the changing

poses. The dashed line of the figure gives the

recognition rate of the PCA system for the images

with different poses. The performance of the

proposed face recognition system is always higher

than the performance of the PCA. Obviously there is

a limit of the angle of planer rotation, where the face

image has been completely changed, e.g. only back

of head is in the image.

Figure 2: Recognition Rate (%) of images for changing

poses.

4 CONCLUSIONS

In this paper we introduced a novel face recognition

system based on grey level histogram matching.

Maximum cross correlation coefficient between the

histogram of a given face and the histograms of the

faces in the database was used for histogram

matching. 98.80% recognition rate on the HP

database of 15 face subjects was obtained by using

the proposed method while this rate was down to

92% in the case of conventional eigenfaces. It has

been shown that due to the high correlation between

the histograms of the faces at different poses, the

proposed system is robust for pose changes.

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