Dual-space Neighborhood Discriminant Embedding for Face Recognition
نویسندگان
چکیده
In this paper, a novel subspace learning method called neighborhood discriminant embedding (NDE) is proposed for pattern classification. In our algorithm, the neighbor and class relations of training samples data are used to construct the low-dimensional embedding submanifold. After being embedding into a low-dimensional subspace, in a local structure, samples from the same class will be as close as possible, and those from different classes are separated far way. However, NDE will suffer from the small sample size problem when dealing with the high dimensional face data. In order to solve the small sample size problem and take fully advantage of the discriminative information in the face space, we further propose a novel algorithm called dual-space neighborhood discriminant embedding (DSNDE). Since Gabor wavelet representation of face images is robust to variations due to illumination and facial expression changes, we apply the proposed DSNDE (NDE) algorithm on Gabor features for face recognition. Experiments on the face databases demonstrate the effectiveness of our method.
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