Skin Segmentation based Elastic Bunch Graph Matching for efficient multiple Face Recognition
This work addresses face recognition efficiency for security or identification systems, but it appears incremental as it combines existing methods.
The paper tackled face detection and recognition by combining skin segmentation with Elastic Bunch Graph Matching (EBGM) to isolate facial regions and create Face Bunch Graphs, resulting in a significant increase in matching probability.
This paper is aimed at developing and combining different algorithms for face detection and face recognition to generate an efficient mechanism that can detect and recognize the facial regions of input image. For the detection of face from complex region, skin segmentation isolates the face-like regions in a complex image and following operations of morphology and template matching rejects false matches to extract facial region. For the recognition of the face, the image database is now converted into a database of facial segments. Hence, implementing the technique of Elastic Bunch Graph matching (EBGM) after skin segmentation generates Face Bunch Graphs that acutely represents the features of an individual face enhances the quality of the training set. This increases the matching probability significantly.