Face Recognition Using Combination of Wavelet Packets, PCA and LDA

In this paper we present a novel approach to recognition of faces in frontal color images. It involves face extraction, creation of face models with wavelet packet decomposition for dimensionality reduction, Principal Component Analysis (PCA) of the decomposed faces, Linear Discriminant Analysis (LDA) over the PCA subspace, neural classifiers with radial basis functions for each modality and combination of classifier results. The first step of the method is face detection through skin-color modeling and segmentation. After the face is extracted, wavelet decomposition is performed. PCA follows the wavelet-packet decomposition, after which further clustering in the subspace is performed using LDA. Then, neural classifiers are created respectively for the wavelet coefficients, PCA projections, and LDA projections. In the end, combination of classifier results is used to improve overall system availability. We tested our approach on a single database. The proposed approach delivered excellent results.