Face Detection using Skin Color Recursive Clustering and Recognition using Multilinear PCA

In this paper, we present a robust approach for face recognition from video sequences. An automatic face detector is employed which uses modified skin color modeling to detect human skin regions from the video sequences. The presence or absence of face in each region is verified by means of height-width proportion and a Neural Network based template-matching scheme. The obtained face images are then projected onto a feature space, defined by Multilinear Principal Component Analysis (MPCA), to produce the biometric feature template. Recognition is performed by projecting a new image onto the feature spaces by the MPCA that generalizes not only the classical PCA solution but also a number of the so-called 2-D PCA algorithms and then classifying the face by comparing its position in the feature spaces with the positions of known individuals. The proposed method is applicable to security systems, secure human computer interaction, visual communication systems (secure video conferencing) and virtual world environments.