Discriminat Components embedded in subspace for face recognition

摘要：

　　Face recognition is a promising biometrics resource. In this paper, for robust and discriminative to change in face recognition, we introduce an algorithm that Linear Discriminant Analysis is applied to the discriminative components, which feature extraction based on the generative probability model and use the distance-based similarity measures for face recognition. XM2VTS dataset is used to validate that the proposed method is superior to the classic algorithms, such as probabilistic Linear Discriminant Analysis, Bayes algorithm and many state-of-theart linear subspace learning (LSL) algorithms. In particular, our method achieves 98% face recognition rate.

关键词： Discriminat Components Linear Discriminant Analysis feature extraction generative probability model face recognition

作者: Guan Y.D. Zhu R.F. Ma G.K. Wang Q.W. Wu M.D.

作者单位: Harbin Institute of Technology,No.92,West Da-Zhi St.,Nan Gang Dist.Harbin,China

会议类型: 国际会议

会议名称: 2015 Fifth International Conference on Instrumentation and Measurement,Computer,Communication and Control (IMCCC2015)(第五届仪器测量、计算机通信与控制国际会议)

会议地点: 秦皇岛

会议语种:英文

页码: 1606-1611

在线出版日期: 2015-09-18（万方平台首次上网日期，不代表论文的发表时间）