A Wavelet-domain Local Dominant Feature Selection Scheme for Face Recognition

In this paper, a multi-resolution feature extraction algorithm for face recognition is proposed based on two-dimensional discrete wavelet transform (2D-DWT), which efficiently exploits the local spatial variations in a face image. For the purpose of feature extraction, instead of considering the entire face image, an entropy-based local band selection criterion is developed, which selects high-informative horizontal segments from the face image. In order to capture the local spatial variations within these high-informative horizontal bands precisely, the horizontal band is segmented into several small spatial modules. Dominant wavelet coefficients corresponding to each local region residing inside those horizontal bands are selected as features. In the selection of the dominant coefficients, a histogram-based threshold criterion is proposed, which not only drastically reduces the feature dimension but also provides high within-class compactness and high between-class separability. A principal component analysis is performed to further reduce the dimensionality of the feature space. Extensive experimentation is carried out upon standard face databases and a very high degree of recognition accuracy is achieved by the proposed method in comparison to those obtained by some of the existing methods.


Introduction
Automatic face recognition has widespread applications in security, authentication, surveillance, and criminal identification.Conventional ID card and password based identification methods, although very popular, are no more reliable as before because of the use of several advanced techniques of forgery and password-hacking.As an alternative, biometric, which is defined as an intrinsic physical or behavioral trait of human beings, is being used for identity access management [5].The main advantage of biometric features is that these are not prone to theft and loss, and do not rely on the memory of their users.Among physiological biometrics, face is getting more popularity because of its non-intrusiveness and high degree of security.Moreover, unlike iris or finger-print recognition, face recognition do not require high precision equipments and user agreement, when doing image acquisition, which make face recognition even more popular for video surveillance.
Nevertheless, face recognition is a complicated visual task even for humans.The primary difficulty in face recognition arises from the fact that different images of a particular person may vary largely, while images of different persons may not necessarily vary significantly.Moreover, some aspects of the image, such as variations in illumination, pose, position, scale, environment, accessories, and age differences, make the recognition task more complicated.However, despite many relatively successful attempts to implement face recognition systems, a single approach, which is capable of addressing the hurdles, is yet to be developed.
Face recognition methods are based on extracting unique features from face images.In this regard, face recognition approaches can be classified into two main categories: holistic and texture-based [13]- [15].Holistic or global approaches to face recognition

International Journal of Computing and Business Research ISSN (Online) : 2229-6166
Volume 2 Issue 3 September 2011 http://www.researchmanuscripts.com involve encoding the entire facial image in a high-dimensional space [13].It is assumed that all faces are constrained to particular positions, orientations, and scales.However, texture-based approaches rely on the detection of individual facial characteristics and their geometric relationships prior to performing face recognition [11], [15].Apart from these approaches, face recognition can also be performed by using different local regions of face images [2]- [4].It is well-known that, although face images are affected due to variations, such as non-uniform illumination, expressions and partial occlusions, facial variations are confined mostly to local regions.It is expected that capturing these localized variations of images would result in a better recognition accuracy [4].In this regard, wavelet analysis is also employed that possesses good characteristics of spatial-frequency localization to detect facial geometric structure [8], [16].Because of the property of shift-invariance, it is well known that wavelet based approach is one of the most robust feature extraction schemes, even under variable illumination [12].

Brief Description of the Proposed Scheme
A typical face recognition system consists of some major steps, namely, input face image collection, pre-processing, feature extraction, classification and template storage or database, as illustrated in Fig. 1.The input image can be collected generally from a video camera or still camera or surveillance camera.In the process of capturing images, distortions including rotation, scaling, shift and translation may be present in the face images, which make it difficult to locate at the correct position.Pre-processing removes any un-wanted objects (such as, background) from the collected image.It may also

Entropy Based Horizontal Band Selection
The information content of different regions of a human face image vary widely [1].It can be shown that, if an image of a face were divided into certain segments, not all the segments would contain the same amount of information.It is expected that a close neighborhood of eyes, nose and lips contains more information than that possessed by the other regions of a human face image.It is obvious that a region with high information content would be the region of interest for the purpose of feature extraction.
However, identification of these regions is not a trivial task.Estimating the amount of information from a given image can be used to identify those significant zones.In this paper, in order to determine the information content in a given area of a face image, an entropy based measure of intensity variation is defined as [9] , = where the probabilities are obtained based on the intensity distribution of the pixels of a segment of an image.It is to be mentioned that the information in a face image exhibits variations more prominently in the vertical direction than that in the horizontal direction [3].Thus, the face image is proposed to be divided into several horizontal bands and the entropy of each band is to be computed.It has been observed from our experiments that variation in entropy is closely related to variation in the face geometry.Volume 2 Issue 3 September 2011 http://www.researchmanuscripts.com possesses a better space-frequency localization.This property of the DWT is helpful for analyzing images, where the information is localized in space.The wavelet transform is analogous to the Fourier transform with the exception that it uses scaled and shifted versions of wavelets and the decomposition of a signal involves sum of these wavelets.
The DWT kernels exhibit properties of horizontal, vertical and diagonal directionality.
The continuous wavelet transform (CWT) of a signal where a is the scale and b is the shift.The DWT coefficients are obtained by restricting the scale ) (a to powers of 2 and the position ) (b to integer multiples of the scales, and are given by where j and k are integers and k j, ψ are orthogonal baby wavelets defined as The approximate wavelet coefficients are the high-scale low-frequency components of the signal, whereas the detail wavelet coefficients are the low-scale high-frequency components.The 2D-DWT of a two-dimensional data is obtained by computing the onedimensional DWT, first along the rows and then along the columns of the data.Thus, for a 2D data, the detail wavelet coefficients can be classified as vertical, horizontal and diagonal detail.
It is intuitive that images of a particular person captured under different lighting conditions may vary significantly, which can affect the face recognition accuracy.In order to overcome the effect of lighting variation in the proposed method, illumination

Proposed Wavelet Domain Dominant Feature
Instead of considering the DWT coefficients of the entire image, the coefficients obtained form each modules of the high-informative horizontal band of a face image are considered to form the feature vector of that image.However, if all of these coefficients were used, it would definitely result in a feature vector with a very large dimension.In view of reducing the feature dimension, we propose to utilize the dominant wavelet coefficients as desired features.In order to select the dominant wavelet coefficients, we propose to consider the frequency of occurrence of the wavelet coefficients as the determining characteristic.It is expected that coefficients with higher frequency of occurrence would definitely dominate over all the coefficients for image reconstruction and it would be sufficient to consider only those coefficients as desired features.One way to visualize the frequency of occurrence of wavelet coefficients is to compute the histogram of the coefficients of a segment of a high-informative horizontal band.In order to select the dominant features from a given histogram, the coefficients having frequency of occurrence greater than a certain threshold value are considered.
It is intuitive that within a high-informative horizontal band of a face image, the image intensity distribution may drastically change at different localities.In order to select the dominant wavelet coefficients, if the thresholding operation were to be performed over the wavelet coefficients of the entire band, it would be difficult to obtain a global threshold value that is suitable for every local zone.Use of a global threshold in a particular horizontal band of a face image may offer features with very low betweenclass separation.In order to obtain high within-class compactness as well as high between-class separability, we have considered wavelet coefficients corresponding to some smaller spatial modules residing within a horizontal band, which are capable of extracting variation in image geometry locally.In this case, for each module, a different

International Journal of Computing and Business Research ISSN (Online) : 2229-6166
Volume 2 Issue 3 September 2011 http://www.researchmanuscripts.com threshold value may have to be chosen depending on the coefficient values of that segment.We propose to utilize the coefficients (approximate and horizontal detail) with frequency of occurrence greater than % θ of the maximum frequency of occurrence for the particular module of the face image and are considered as dominant wavelet coefficients and selected as features for the particular segment of the image.This operation is repeated for all the modules of a face image within the selected highinformative horizontal band.
Next, in order to demonstrate the advantage of extracting dominant wavelet coefficients corresponding to some smaller modules residing in a horizontal band, we conduct an experiment considering two different cases: ( i ) when the entire horizontal band is used as a whole and ( ii ) when all the modules of that horizontal band are used separately for feature extraction.For these two cases, centroids of the dominant approximate wavelet coefficients obtained from several poses of two different persons (appeared in Fig. 6) are computed and shown in Figs.7 and 8, respectively.It is observed from Fig. 7

Distance Based Face Recognition
In the proposed method, for the purpose of recognition using the extracted dominant features, a distance-based similarity measure is utilized.The recognition task is carried out based on the distances of the feature vectors of the training face images from the feature vector of the test image.Given the m -dimensional feature vector for the k -th pose of the j -th person be )} ( (2),..., (1) , a similarity measure between the test image f of the unknown person and the sample images of the j -th person, namely average sumsquares distance, ∆ , is defined as where a particular class represents a person with q number of poses.Therefore, according to (5), given the test face image f , the unknown person is classified as the person j among the p number of classes when } {1,2,..., , p g and g j 4 Experimental Results Extensive simulations are carried out in order to demonstrate the performance of the proposed feature extraction algorithm for face recognition.In this regard, different wellknown face databases have been considered, which consist a range of different face images varying in facial expressions, lighting effects and presence/absense of accessories.The performance of the proposed method in terms of recognition accuracy is obtained and compared with that of some recent methods [10,6].

Performance Comparison
In the proposed method, dominant features (approximate and horizontal detail 2D-DWT coefficients) obtained from all the modules of high-informative horizontal bands of a face image are used to form the feature vector of that image and feature dimension reduction is performed using PCA.The recognition task is carried out using a simple Euclidean distance based classifier as described in Section 3.5.The experiments were performed following the leave-one-out cross validation rule.
For simulation purposes, N number of horizontal bands are selected based on the entropy measure described in Section 3.1 and divided further into small modules.For the purpose of comparison, recognition accuracies obtained using the proposed method along with those obtained by the methods reported in [10] and [6] are listed in Table 1.Here, in case of the ORL database, the recognition accuracy for the method in [6] is denoted as not available (N/A).It is evident from the table that the recognition accuracy of the proposed method is comparatively higher than those obtained by the other methods for both the databases.It indicates the robustness of the proposed method against partial occlusions, expressions and nonlinear lighting variations.Method [10] 98.18% 99.00% Method [6] 97.70% N/A

Conclusions
The proposed wavelet-based dominant feature extraction algorithm provides an excellent space-frequency localization, which is clearly reflected in the high within-class compactness and high between-class separability of the extracted features.Instead of using the whole face image for feature extraction at a time, first, certain high-informative horizontal bands within the image are selected using the proposed entropy based measure.Modularization of the horizontal bands is performed and the dominant wavelet coefficient features are then extracted from within those local zones of those horizontal bands.It has been found that the proposed feature extraction scheme offers an advantage of precise capturing of local variations in the face images, which plays an important role in discriminating different faces.Moreover, it utilizes a very low dimensional feature space, which ensures lower computational burden.For the task of classification, an Euclidean distance based classifier has been employed and it is found that, because of the quality of the extracted features, such a simple classifier can provide a very satisfactory recognition performance and there is no need to employ any complicated classifier.From our extensive simulations on different standard face databases, it has been found that the proposed method provides high recognition accuracy even for images affected due to partial occlusions, expressions and nonlinear lighting variations.

Volume 2
Hence, it is motivating to utilize local variations of face geometry using wavelet transform for feature extraction and thereby develop a face recognition scheme incorporating the advantageous properties of both holistic-and texture-based approaches.The objective of this paper is to develop a wavelet-based face recognition scheme, which, instead of the entire face image, considers only some high-informative local zones of the image for dominant feature extraction.An entropy based horizontal band selection criterion is developed to exploit the high-informative areas of a face image.In order to precisely capture the local spatial variation within a high-informative horizontal band, such high-informative bands are further divided into some smaller spatial modules.We propose to extract dominant wavelet coefficients corresponding to some smaller segments residing within the band utilizing a histogram-based on a threshold criterion.In comparison to the discrete Fourier transform, the DWT is used as it possesses a better space-frequency localization.In view of further reducing the computational complexity, principal component analysis is performed on the proposed International Journal of Computing and Business Research ISSN (Online) : 2229-6166 Issue 3 September 2011 http://www.researchmanuscripts.com feature space.Finally, the face recognition task is carried out using a distance based classifier.
segment the face image for feature extraction.For the purpose of classification, an image database is needed to be prepared consisting template face poses of different persons.The recognition task is based on comparing a test face image with template data.It is obvious that considering images themselves would require extensive computations for the purpose of comparison.Thus, instead of utilizing the raw face images, some characteristic features are extracted for preparing the template.It is to be noted that the recognition accuracy strongly depends upon the quality of the extracted features.Therefore, the main focus of this research is to develop an efficient feature extraction algorithm.The proposed feature extraction algorithm is based on extracting spatial variations precisely from high informative local zones of the face image instead of utilizing the entire image.In view of this, an entropy based selection criterion is developed to select high informative facial zones.A modularization technique is employed then to segment the high informative zones into several smaller segments.It should be noted that variation of illumination of different face images of the same person may affect their similarity.Therefore, prior to feature extraction, an illumination adjustment step is included in the proposed algorithm.After feature extraction, a classifier compares International Journal of Computing and Business Research ISSN (Online) : 2229-6166 Volume 2 Issue 3 September 2011 http://www.researchmanuscripts.com features extracted from face images of different persons and a database is used to store registered templates and also for verification purpose.

Figure 1 :
Figure 1: Block diagram of the proposed method

Fig. 2 (
Figure 2: (a) Sample face image of a person and (b) entropy values in different horizontal bands of several face poses

Figure 3 :Figure 4 :Volume 2 Figure 5 :
Figure 3: Two face images of the same person under different illumination

Volume 2 Figure 6 :Figure 7 :
Figure 6: Sample face images of two persons

Figure 8 :Volume 2
Figure 8: Feature centroids of different poses for modularized horizontal band

Figure 9 :
Figure 9: Feature values for un-modularized horizontal band

Figure 10 :Volume 2
Figure 10: Feature values for modularized horizontal band

Figure 11 :Figure 12 :Volume 2
Figure 11: Sample poses of a person from the ORL database Module height is the same as that of the horizontal band and module width is chosen based on the face image width.In our simulations, database are chosen and the module sizes are chosen as 16 16× pixels and 32 32× pixels, respectively.The dominant wavelet coefficients corresponding to all the local segments residing in the horizontal bands are then obtained using 10 = θ .

Table 1 :
Comparison of recognition accuracies