Complex Wavelet Transform-Based Face Recognition
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Research Article Complex Wavelet Transform-Based Face Recognition ¨ ¨ Alaa Eleyan, Huseyin Ozkaramanli, and Hasan Demirel Electrical & Electronic Engineering Department, Eastern Mediterranean University, Famagusta, Northern Cyprus, 10-Mersin, Turkey Correspondence should be addressed to Alaa Eleyan, [email protected] Received 1 September 2008; Accepted 19 December 2008 Recommended by Jo˜ao Manuel R. S. Tavares Complex approximately analytic wavelets provide a local multiscale description of images with good directional selectivity and invariance to shifts and in-plane rotations. Similar to Gabor wavelets, they are insensitive to illumination variations and facial expression changes. The complex wavelet transform is, however, less redundant and computationally efficient. In this paper, we first construct complex approximately analytic wavelets in the single-tree context, which possess Gabor-like characteristics. We, then, investigate the recently developed dual-tree complex wavelet transform (DT-CWT) and the single-tree complex wavelet transform (ST-CWT) for the face recognition problem. Extensive experiments are carried out on standard databases. The resulting complex wavelet-based feature vectors are as discriminating as the Gabor wavelet-derived features and at the same time are of lower dimension when compared with that of Gabor wavelets. In all experiments, on two well-known databases, namely, FERET and ORL databases, complex wavelets equaled or surpassed the performance of Gabor wavelets in recognition rate when equal number of orientations and scales is used. These findings indicate that complex wavelets can provide a successful alternative to Gabor wavelets for face recognition. Copyright © 2008 Alaa Eleyan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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INTRODUCTION
Identifying a person using geometric or statistical features derived from a face image is an important and challenging task [1–3]. This task becomes even more challenging due to the fact that large variations in the visual stimulus arising from illumination condition, viewing directions, poses, facial expression, aging, disguises are all common in real applications. A face recognition system should, to a large extent, take into account all the above-mentioned natural constraints and cope with them in an effective manner. In order to achieve this, one must have efficient and effective representations for faces. It is important that the representation of face images have the following desirable properties. (1) It should require minimum or no manual annotations, so that the face recognition task can be performed automatically; (2) representation should not be redundant. In other words, the feature vector representing the face image should contain critical amount of information in order to make sure that the dimensionality of the representation is minimal; (3) the represent
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