An Automatic Face Detection System for RGB Images
AbstractWe propose a robust face detection approach that works for digital color images. Our automatic detection method is based on image skin regions, therefore a skin-based segmentation of RGB images is provided first. Then, we decide for each skin region if it represents a human face or not, using a set of candidate criteria, an edge detection process, a correlation based technique and a threshold-based method. A high face detection rate is obtained using the proposed method.
 M.H. Yang, D. Kriegman, N. Ahuja. Detecting Faces in Images: A Survey, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Vol. 24, no. 1, pp. 34-58, Jan. 2002.
 S. Atsushi, I. Hitoshi, S. Tetsuaki, H. Toshinori. Advances in face detection and recognition technologies, NEC Journal of Advanced Technology, Vol. 2, no. 1, pp. 28-34, 2005.
 T. Barbu. Eigenimage-based face recognition approach using gradient covariance, Numerical Functional Analysis and Optimization, Volume 28, pp. 591 . 601, Issue 5 & 6, May 2007.
 G. Yang, T.S. Huang. Human face detection in a complex background. Pattern Recognition, Vol. 27, no. 1, pp. 53-63, 1994.
 T.K. Leung, M.C. Burl, P. Perona. Finding Faces in Cluttered Scenes Using Random Labeled Graph Matching, Proceedings of the 5th International Conference on Computer Vision, pp. 637-644, Cambridge, Mass., June 1995.
 K.C. Yow, R. Cipolla. A probabilistic framework for perceptual grouping of features for human face detection, Second IEEE International Conference on Automatic Face and Gesture Recognition (FG '96), pp. 16, 1996.
 H.A. Rowley, S. Baluja, T. Kanade. Neural Network-Based Face Detection, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 203-208, 1996.
 A.V. Nefian. An embedded HMM-based approach for face detection and recognition, Proceedings of the Acoustics, Speech, and Signal Processing ďż˝e99 on 1999 IEEE International Conference, Vol. 6, pp. 3553-3556, 1999.
 T.V. Pham, M. Worring, A.W.M. Smeulders. Face Detection by Aggregated Bayesian Network Classifiers, Machine Learning and Data Mining in Pattern Recognition, Book Series Lecture Notes in Computer Science, Volume 2123, pp. 249-262, 2001.
 E. Osuna, R. Freund, F. Girosi. An improved training algorithm for support vector machines, In Proceedings of IEEE NNSP'97, pp. 276-285, Amelia Island, Florida, 1997 (a).
 M. Nilsson, J. Nordberg, I. Claesson. Face Detection using Local SMQT Features and Split Up SNoW Classifier, IEEE International Conference on Acoustics, Speech, and Signal Processing
 K. Ichikawa, T. Mita, O. Hori. Component-based robust face detection using AdaBoost and decision tree, Proc. of the 7th Int. Conference on Automatic Face and Gesture Recognition, pp. 413-420, 2006.
 Z. Jin, Z. Lou, J. Yang, Q. Sun. Face detection using template matching and skin-color information, Advanced Neurocomputing Theory and Methodology, Vol. 70, Issues 4-6, pp. 794-800, Jan. 2007.
 S. Majed, H. Arof. Pattern correlation approach towards face detection system framework, Information Technology, 2008. ITSim 2008. International Symposium on, Vol. 4, pp. 1-5, Aug. 2008.
 D. A. Forsyth, M. M. Fleck. Identifying nude pictures, IEEE Workshop on the Applications of Computer Vision '96, pp. 103-108, 1996.
 V. Vezhnevets, V. Sazonov, A. Andreeva. A Survey on Pixel-Based Skin Color Detection Techniques, In Proceedings of the GraphiCon 2003, pp. 85-92, 2003.
 L.G. Shapiro, G. C. Stockman. Computer Vision, pp. 137- 150, Prentince Hall, 2001.
 H.J.A.M. Heijmans. Morphological Image Operators, Advances in Electronics and Electron Physics, Boston: Academic Press, 1994.
 J. Canny, A Computational Approach To Edge Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 88, pp. 679-714, 1986.
 A.L. Edwards, An Introduction to Linear Regression and Correlation, San Francisco, CA: W.H. Freeman, pp. 33-46, 1976.
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