Multi‐step linear representation‐based classification for face recognition

Abstract

Error detection is an important approach to improve the robustness of face recognition method. However, it is hard to directly detect the invalid pixels in a facial image. The authors decompose the hard problem into many simpler sub‐problems in this study. That is, the error detection process of pixels is divided into multiple phases and a portion of invalid pixels are detected in each phase. The goal is to decrease the ratio of invalid pixels to the whole pixels in a testing image, which progressively improves the final recognition accuracy. The performance that their method deals with occlusion and corruption problems is evaluated on different databases. In addition, the comparison with other state‐of‐the‐art studies shows that the proposed method achieves the best results in face occlusion and disguise issues.

Keywords

• multistep linear representation-based classification
• face recognition
• invalid pixel detection
• pixel error detection process
• testing image
• corruption problem