Abstract
Since Gabor features are robust to changes in illumination and facial expression and have been successfully applied for face recognition. The locality preserving projection (LPP) is nonorthogonal and makes it difficult to reconstruct the data. The orthogonal locality preserving projection (OLPP) produces orthogonal basis functions and can have more locality preserving power than LPP. OLPP has more discriminating power than LPP. Therefore, our face recognition algorithm using Gabor wavelet and OLPP is proposed. First, Gabor wavelets extract Gabor features from face images, then OLPP reduces the dimensionality of the Gabor feature vectors, finally, the nearest neighbor classifier is adopted for classification and recognition. The proposed algorithm is experimented on ORL and Yale databases, the best recognition rates of the algorithm are 97.5% and 100% respectively. Obviously, the experimental results demonstrate the effectiveness of our proposed algorithm.
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