This article proposes a novel face-recognition algorithm that uses a single training face image.
The algorithm is based on textural features extracted using the 2D log Gabor wavelet. These features are encoded into a binary pattern to form a face template which is used for matching. Experimental results show that on the color FERET database the accuracy of the proposed algorithm is higher than the local feature analysis (LFA) and correlation filter (CF) based face recognition algorithms even when the number of training images is reduced to one. In comparison with recent single training image-based face recognition algorithms, the proposed 2D log Gabor wavelet-based algorithm shows an improvement of more than 3% in accuracy. (Published Abstract Provided)
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