提出了一人基于小波变换和Fourier变换的人像识别新方法，它首先对人像作适当层数的二维小波分解，然后对其低频的子图象作Fourier变换，从而获得原人像的一个低维空间的表达，该表达是振幅谱位移不变的。在Yale和Olivetti人像数据库上的实验表明，频谱脸的方法比PCA的方法和空间模式匹配法有更佳的识别效果，特别是它能有效地消除因为人像的表情变化和少许遮掩带来的识别误差。

This paper presents a new face recognition method in Fourier domain. The proposed method combines the Fourier transform and the wavelet transform for face recognition. Yale and Olivetti face image databases are selected to evaluate the proposed method. The results show that the proposed method gives higher accuracy than PCA method and template matching in spatial domain. Spectroface is extremely effective for eliminating the errors that bring from different expressions and small occlusion.