New parallel models for face recognition

Subspace methods such as principal component analysis (PCA) and linear discriminant analysis (LDA) extract the features based on space domain. Transformation such as discrete cosine transform (DCT) extracts features based on frequency domain. In this paper, we present two parallel models which intend to utilize the features extracted from frequency and space domain of facial images. Both features are combined under a fusion based scheme. FERET database is chosen to evaluate the performance of the proposed method. Simulation results indicate that the proposed method outperforms other traditional methods and enhance the representation of facial image under low-dimensional features.