Face and non-face classification by multinomial logit model and kernel feature compound vectors

Abstract

This paper introduces a method for face and non-face classification. The method is based on the combined use of the multinomial logit model (MLM) and "kernel feature compound vectors". The NMM is one of the neural network models for multi-class 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-ace 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 the best compared with the results by the Support Vector Machines (SVM) and the Kernel Fisher Discriminant Analysis (KFDA).