Extraction Of Head And Face Boundaries For Face Detection Application Face detection is an importent first step to many advanced computer vision, biometrics and multimedia applications such as face tracking, face recognition and video surveillance. In this paper, a faster face detection system is proposed and the method of extracking head and face boundaries along with its facial features has been utilized. Initially, boundary tracking is employed to extract the head and face boundaries from the image. This boundary tracking is done with the help of BW (Black and White) tracking function. Facial features are extracted using gabor filter algorithm. The neural network employed for face detection is based on multi layer neurons architecture while is a feed forword network. This approch is even applicable for detecting faces in cluster images. Experimental results show that the proposed approach can perfrom the extraction human head, face boundaries and detection of face succesfully. The proposed technique can be applied for images with single face as well as nultiple faces and the faces are detected succesfully with high detection rate when compared to the adaboost technique of face detection.
Extraction Of Head And Face Boundaries For Face Detection.pdf ...
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Abstract. Face recognition is technique which is used to identify person by their face. Identifying the person is performed with different types of Biometric like iris scan, finger print, face recognition, Gait recognition; Signature etc. face recogn
There are many problems are occurred when it perform face recognition like illumination, light intensity, blurred face, noisy image, tilted face, different head pose ...
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A grid-based and dense extraction of local features in combination with a block-based matching ... Usually, a main drawback of an interest point based fea- .... navente at the Computer Vision Center of the University of Barcelona [13].
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Human Language Technology and ..... In CVPR, Miami, FL, USA, June. 2009. ... In International Conference on Automatic Face and Gesture Recognition,. 2002.
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