Face Recognition System Based on Edge Detection

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      A face detection algorithm very robust against illumination, focus and scale variations in input images has been developed based on the edge-based image representation. The multiple-clue face detection algorithm developed in our previous work has been employed in conjunction with a new decision criterion called idensity rule, where only high density clusters of detected face candidates are retained as faces. As a result, the occurrence of false negatives has been greatly reduced. The robustness of the algorithm against circumstance variations has been demonstrated.

      The automatic recognition of human faces presents a significant challenge to the pattern recognition research community, 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 detection”. A face pre filtering 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 most of the existing comparison experiments.

Edge and Edge detector:

  An edge in an image is a contour across which the brightness of the image changes abruptly. In image processing, an edge is often interpreted as one class of singularities. In a function, singularities can be characterized easily as discontinuities where the gradient approaches infinity. However, image data is discrete, so edges in an image often are defined as the local maxima of the gradient. Edge detection is an important task in image processing. It is a main tool in pattern recognition, image segmentation, and scene analysis. An edge detector is basically a high pass filter that can be applied to extract the edge points in an image.


Varies view of face images with different illumination conditions
Edge information's of face images




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