Infrared Face Recognition based on DCT and Partial Least Squares

摘要：

　　Infrared face imaging, being light-independent, and not vulnerable to facial skin expressions and posture, can avoid or limit the drawbacks of face recognition in visible light. However, to obtain the compact and discriminative feature extracted from infrared face image is a challenging task. In this essay, infrared face recognition method using Discrete Cosine Transform (DCT) and Partial Least Square (PLS) is proposed. Due to strong ability for data de-correlation and compact energy, DCT is studied to obtain the compact features in infrared face. To make full use of the discriminative information in DCT coefficients, the final classifier formulates PLS regression for accurate classification. The experimental results show that the proposed algorithm outperforms Principle Component Analysis (PCA) and DCT based infrared face recognition algorithms.

关键词： Infrared face recognition Partial least square feature extraction discrete cosine transform

作者: Zhihua Xie Guodong Liu

作者单位: Key Lab of Optic-Electronic and Communication, Jiangxi Sciences and Technology Normal University, Nanchang, Jiangxi, 330013, China

会议类型: 国际会议

会议名称: 9th Conference on Image and Graphics Technologies and Applications(IGTA2014)(第九届图像图形技术与应用学术会议)

会议地点: 北京

会议语种:英文

页码: 71-78

在线出版日期: 2014-06-01（万方平台首次上网日期，不代表论文的发表时间）