A Novel Discriminant Analysis Approach Using Angular Fourier Transform for Face Recognition

• Main Authors: Xiaoyuan Jing, Lin Liu, Sheng Li, Yongfang Yao, Lusha Bian, Qian Liu, Yong Dong, Zaijuan Sui
• Format: Conference Proceeding
• Language: English
• Subjects: angle parameter; Angular Fourier transform; Data mining; Face recognition; Feature extraction; Fourier transforms; Frequency domain analysis; Image analysis; Image databases; improved Fisherface method; Information analysis; Nearest neighbor searches; Signal processing
• DOI: 10.1109/IITA.2009.100

In this paper, a novel discriminant analysis approach using Angular Fourier transform is proposed for face recognition. As a generalization of Fourier transform, the Angular Fourier transform is an important frequency-domain analysis technique. The proposed approach combines it with discriminant analysis method. First, this approach selects appropriate value of angle parameter for discrete Angular Fourier transform by using 2D separability judgment, and then it uses an improved Fisherface method to extract discriminative features from the preprocessed images. Finally, the nearest neighbor classifier is employed for classification. Using a public face databases as the test data, the experimental results demonstrate that the proposed approach outperforms several related discrimination methods.