Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
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An introduction to support Vector Machines: and other kernel-based learning methods
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
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MOTION '02 Proceedings of the Workshop on Motion and Video Computing
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
ACM Computing Surveys (CSUR)
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IITA '07 Proceedings of the Workshop on Intelligent Information Technology Application
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AVSS '08 Proceedings of the 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance
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CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
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Human brain can detect faces from the images constructed in their eyes. The face detection is a computerize method of locating the face in the digital image. It is an important challenge to locate faces from uncontrolled and indistinguishable background of the digital image. This paper presents human face detection from the colored images. Skin color segmentation is used for localizations of skin colored components in the digital image. The features are extracted by using 2D-Discrete Cosine Transform (2D-DCT). The Back Propagation Neural Network (BPN) and Self Organization Map (SOM) are used for training and testing phases. In this research, total of 180 images have been used. About 60% of the images are used for training phase and 40% of the images are used for testing phase. The best detection rate has been obtained from BPN as 85.29% with the false positive rate of 6.18. However the best false positive rate is obtained from BPN as 5.05 with detection rate of 84.03%.These results are better than the results of existing methods of face detection using 2D-DCT