Discriminative Zernike and Pseudo Zernike Moments for Face Recognition

Discriminative Zernike and Pseudo Zernike Moments for Face Recognition

Chandan Singh, Ekta Walia, Neerja Mittal
Copyright: © 2012 |Pages: 24
DOI: 10.4018/ijcvip.2012040102
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Usually magnitude coefficients of some selected orders of ZMs and PZMs have been used as invariant image features. The careful selection of the set of features, with higher discrimination competence, may increase the recognition performance. In this paper, the authors have used a statistical method to estimate the discrimination strength of all the extracted coefficients of ZMs and PZMs whereas for classification, only the coefficients with estimated higher discrimination strength are used in the feature vector. The performance of these selected Discriminative ZMs (DZMs) and Discriminative PZMs (DPZMs) features have been compared to that of their corresponding conventional approaches on YALE, ORL and FERET databases against illumination, expression, scale and pose variations. An extension to these DZMs and DPZMs have been proposed by combining them with PCA and FLD. It has been observed from the exhaustive experimentation that the recognition rate is improved by 2-6%, at reduced dimensions and with less computational complexity, than that of using the successive ZMs and PZMs features.
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1. Introduction

The main objective of face recognition system is to identify a person in the database of stored face images. These days it is one of the most popular research topics because of having many commercial and law enforcement applications, e.g., criminal identification at public places, human computer interaction, video surveillance and bankcard verification, etc. Although, there exist some good reliable methods of human identification such as iris recognition, fingerprint recognition, etc., yet these methods depend on the cooperation of individuals, whereas a face recognition system can work without the support or knowledge of the persons. Most of the face recognition approaches lack in satisfactory results under the conditions of lighting directions, pose variation, aging, occlusion and real world problems, etc. (Zhao et al., 2003).

In literature, the existing face recognition approaches are broadly classified into two categories namely local methods and the global methods (Hjelmas & Low, 2001). In local methods, structural features of facial shape like eyes, nose and mouth are analyzed. These methods are not affected by the irrelevant information in the images, but are sensitive to unpredictability of face appearance and noise. Global methods are statistical based methods which use the complete information of the faces and are less sensitive to noise. Principal component analysis (PCA) (Burton et al., 2005; Neerja & Walia, 2008; Moon & Phillips, 2001; Turk, 2001), Fisher linear Discriminant (FLD) (Belhumeur et al., 2005; Etemad & Chellappa, 1997; Martinez & Kak, 2001), Two-Dimensional PCA (2DPCA) (Xu et al., 2008), Two-Directional Two-Dimensional PCA (2D2PCA) (Daoqiang & Zhi-Hua, 2005) and the moment invariants (Singh, 2006; Teh & Chin, 1988; Wee & Paramesran, 2007) fall under the category of global methods. Orthogonal radial moments and their functions have been used as useful image features in a number of applications (Teague, 1980). Among these orthogonal radial moment based methods, Zernike moments (ZMs) and pseudo-Zernike moments (PZMs) are particularly useful because they are less susceptible to information redundancy and image noise. The magnitude of these orthogonal radial moments is invariant to rotation and under certain geometric transformations they can be made invariant to scale and translation. Some work has been already done to present the usefulness of moment based invariant features for the recognition of face images (Nor’aini et al., 2006; Singh et al., 2011; Singh et al., 2011). A comparative analysis of different moment invariants is presented (Faez & Farajzadeh, 2006; Nabatchian et al., 2008). Nor’aini et al. (2006) has observed that the ZMs are able to recognize face images better than PCA because of its characteristics, of being invariant to rotation and insensitivity to image noise. PZMs using neural network classifier (Haddandnia et al., 2003) and the ZMs/ PZMs using optimal similarity measure (Singh et al., 2011) gave better results as compared to the Euclidean distance classifier.

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