关键词: 人脸对齐, 人脸识别, 旋转不变, 对称PCA

Abstract: With the rapid development of face recognition algorithms in many applications, more and more attentions have been paid to the face alignment algorithm which is the intermediate step between face detection and face recognition. A Transformation Invariant Symmetrical Principal Components Analysis（TI-SPCA） framework is proposed to automatically align and recognize facial images. Different from the traditional eye-aligned methods, TI-SPCA alignment needs no human intervention. Furthermore, TI-SPCA obtains a transform invariant feature space by minimizing the error between reconstructed images and distorted images. To compare its performance against the eye-aligned method and show its outperformance, this work intuitively demonstrate the visual performance of alignment through the output images of two different alignment methods on ORL database and FERET database. Finally, in order to verify the validity of the aligned image in the recognition system, the proposed method is evaluated by combining three distance functions and four local operators. The experimental results show the effectiveness of automatic alignment method based on TI-SPCA in face recognition.

Key words: face alignment, face recognition, transform invariant, symmetrical principal components analysis