A kernel logit approach for face and non-face classification

This paper introduces a kernel logit approach for face and non-face classification. The approach is based on the combined use of the multinomial logit model (MLM) and "kernel feature compound vectors." The MLM is one of the neural network models for multiclass pattern classification, and is supposed to be equal or better in classification performance than linear classification methods. The "kernel feature compound vectors" are compound feature vectors of geometric image features and Kernel features. Evaluation and comparison experiments were conducted by using face and non,face images (Face: training 100, cross-validation 300, test 325, Non-face : training 200, cross-validation 1000, test 1000) gathered from the available face databases and others. The experimental result obtained by the proposed method was better than the results obtained by the Support Vector Machines (SVM) and the Kernel Fisher Discriminant Analysis (KFDA).