Face retrieval using 1st- and 2nd-order PCA mixture model

This paper deals with face retrieval using the 1st- and 2nd-order PCA mixture model. The well-known eigenface method uses one set of holistic facial features obtained by PCA. However, the single set of eigenfaces is not enough to represent face images with large variations. To overcome this weakness, we propose the method that uses more than one set of eigenfaces obtained from the EM learning in PCA mixture model. 2nd-order eigenface method can be extended to the method using 2nd-order PCA mixture model, also. Simulation results show that the method using 2nd-order PCA mixture model is the best for the face images with illumination variations and the method using 1st-order PCA mixture model is the best for the face images with pose variations in, terms of ANMRR (average of the normalized modified retrieval rank).