Face recognition using line edge map
The automatic recognition of human faces presents a significant challenge to the pattern recognition research community. Typically, human faces are very similar in structure with minor differences from person to person. They are actually within one class OF "human face." Furthermore, lighting condition changes, facial expressions, and pose variations further complicate the face recognition task as one of the difficult problems in pattern analysis. This paper proposed a novel concept, "faces can be recognized using line edge map." A compact face feature, Line Edge Map (LEM), is generated for face coding and recognition. A thorough investigation on the proposed concept is conducted which covers all aspects on human face recognition, 1,e., face recognition, under 1) controlled/ideal condition and size variation, 2) varying lighting condition, 3) varying facial expression, and 4) varying pose. The system performances are also compared with the eigenface method, one of the best face recognition techniques, and reported experimental results of other methods. A face prefiltering technique is proposed to speed up the searching process. It is a very encouraging finding that the proposed face recognition technique has performed superior to the eigenface method in most of the comparison experiments. This research demonstrates that LEM together with the proposed generic line segment Hausdorff distance measure provide a new way for face coding and recognition.
face recognition line edge map line segment hausdorff distance structural information