FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
Average-half-face in 2D and 3D using wavelets for face recognition
SIP'10 Proceedings of the 9th WSEAS international conference on Signal processing
Image quality assessment by discrete orthogonal moments
Pattern Recognition
A wavelet-based face recognition system using partial information
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
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This paper presents a novel method of combining Wavelet Transforms (WT) and Zernike Moments (ZM) as a feature vector for face recognition. Wavelet transform, with its approximate decomposition is used to reduce the noise and produce a representation in the low frequency domain, and hence making the facial images insensitive to facial expression and small occlusion. The Zernike Moments, on the other hand, is selected as feature extractor due to its robustness to image noise, geometrical invariants property and orthogonal property. The simulation results on Essex Database indicates that higher order degree of WT combine with ZM achieve better performance with respect to recognition rate rather than using WT or ZM alone. The optimum result is obtained for ZM of order 10 with Daubechies orthonormal wavelet filter of order 7 in the first decomposition level. It can achieve the verification of 94.26%.