Pose Invariant Face Recognition Based on Hybrid Dominant Frequency Features

I Gede Pasek Suta WIJAYA  Keiichi UCHIMURA  Zhencheng HU  

IEICE TRANSACTIONS on Information and Systems   Vol.E91-D    No.8    pp.2153-2162
Publication Date: 2008/08/01
Online ISSN: 1745-1361
DOI: 10.1093/ietisy/e91-d.8.2153
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Pattern Recognition
face feature,  matching,  DCT,  DWT,  metrics,  eigenface,  

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Face recognition is one of the most active research areas in pattern recognition, not only because the face is a human biometric characteristics of human being but also because there are many potential applications of the face recognition which range from human-computer interactions to authentication, security, and surveillance. This paper presents an approach to pose invariant human face image recognition. The proposed scheme is based on the analysis of discrete cosine transforms (DCT) and discrete wavelet transforms (DWT) of face images. From both the DCT and DWT domain coefficients, which describe the facial information, we build compact and meaningful features vector, using simple statistical measures and quantization. This feature vector is called as the hybrid dominant frequency features. Then, we apply a combination of the L2 and Lq metric to classify the hybrid dominant frequency features to a person's class. The aim of the proposed system is to overcome the high memory space requirement, the high computational load, and the retraining problems of previous methods. The proposed system is tested using several face databases and the experimental results are compared to a well-known Eigenface method. The proposed method shows good performance, robustness, stability, and accuracy without requiring geometrical normalization. Furthermore, the purposed method has low computational cost, requires little memory space, and can overcome retraining problem.