Face recognition using surface features in XYI space

We propose a face recognition algorithm that utilizes novel surface features in (x, y, I(x,y)) space. A face image is considered as a surface in XYI space, and the surface is segmented into a definite number of regions by using a Gaussian mixture model. Parameters of each Gaussian distribution are determined by maximizing the log-likelihood function, and are stored as features of each individual face image. In the recognition process, the log-likelihood is used as a similarity measure between a test image and the stored features. The face recognition performance of our algorithm is evaluated with the FERET database. Our algorithm achieves an identification rate of 95.4% and equal error rate of 1.4%, which are superior to other algorithms based on eigenface features and Gabor wavelet features.