Finishing the Classifier Design of Spectroface Human Face Recognition
完善频谱脸人像识别的分类器设计

Spectroface is a face representation method using wavelet transform and Fourier transform and have been prove to be invariant to translation and tolerant to expression variety. In this paper, the two important issues on Spectroface system is studied. One is how to preprocess system, another is the selection of similarity measurement. The moment is employed to preprocess system that it is good method to normalizing the scale and rotation of human face. The similarity measurement has been selected by comparing four typical kinds of similarity measurement, i.e., nearest neighbor method, averaging method, Hausdorff distance method and modified Hausdorff distance method(MH). Nearest neighbor method, averaging method and modified Hausdorff distance method are good for Spectroface. Nearest neighbor method is the most effective method in the recognition of frontal faces with translation, scale, rotation, different facial expressions, small pose, small occlusion and different illumination condition. It gives high accuracy as 97% and 99% in Yale and Olivetti face image databases respectively.