FACE RECOGNITION USING CURVELET-BASED TWO-DIMENSIONAL PRINCIPLE COMPONENT ANALYSIS

作者：Zhang, Yan^*; Yu, Bin; Gu, Hai-Ming

来源：International Journal of Pattern Recognition and Artificial Intelligence, 2012, 26(3): 1256009.

DOI：10.1142/S0218001412560095

• Curvelet transforms
• face recognition
• pattern analysis
• Support Vector Machine

The task of face recognition has been actively researched in recent years because of its many applications in various domains. This paper presents a robust face recognition system using curvelet-based two-dimensional principle component analysis (2D PCA) to address the problem of human face recognition from still images. 2D PCA has advantages over PCA in evaluating the covariance matrix accurately and time complexity. Inspired by the attractive attributes of curvelets in catching the edge singularities with very few coefficients in a non-adaptive manner, we introduce the scheme of decomposing images into curvelet subbands and applying 2D PCA to create a representative feature set. Experiments were designed with different implementations of each module using standard testing database. We experimented with changing the illumination normalization procedure; comparing the baseline PCA-based method with the proposed scheme; studying effects on algorithm performance of k-nearest neighbor (kNN) classiffier and Support Vector Machine (SVM) classiffier in the classiffication process; also we experimented with different databases such as FERET, etc. High accuracy rate were achieved by the proposed scheme through a comparative study.

• 出版日期2012-5
• 单位青岛科技大学