Artificial Intelligent Systems and Machine Learning

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Existing Methods for Face Detection:A Review


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1 SITRC, Nashik, India
     

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Face detection and recognition owned significant consideration and appreciated as one of the most promising applications in the field of image processing. Face detection can consider a substantial part of face recognition operations The method of face detection in pictures is complicated because of variability present across human faces such as pose, expression, position and orientation, skin color, the presence of glasses or facial hair, differences in camera gain, lighting conditions, and image resolution. In this paper, various face detection algorithms are discussed which are frequently used. The Viola-Jones face detector is first studied. After that we survey number of techniques according to how they extract features and what learning algorithms are implemented.


Keywords

Face Detection, Classifier, Viola-Jones Face Detector, Local Binary Pattern, Hidden Markov Model, Support Vector Machine, Principal Component Analysis, Neural Network-Based Face Detection.
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  • Cha Zhang and Zhengyou Zhang, “A Survey of Recent Advances in Face Detection”, June 2010.

  • M.-H. Yang, D. J. Kriegman, and N. Ahuja, “Detecting faces in images: A survey”, IEEE Trans. on PAMI, 24(1):34–58, 2002.

  • P.Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features”, In Proc. of CVPR, 2001.

  • H.A. Rowley, S. Baluja, and T. Kanade, “Neural Networks Based Face Detection”, IEEE Trans. Pattern Analysis an Machine Intelligence, vol. 20, no. 1, pp. 22-38, Jan. 1998.

  • Ahonen T, Hadid A, Pietika¨inen M, ”Face recognition with local binary patterns”, In Proceeding of European conference on computer vision (ECCV2004), LNCS 3021, pp 469–481,2004

  • Ingvar Claesson, "Face Detection using Local SMQT Features and Split up Snow Classifier", 2007 IEEE International Conference on Acoustics Speech and Signal Processing-ICASSP 07, 04/2007

  • G. Yang and T. S. Huang, “Human Face Detection in Complex Background”, Pattern Recognition, vol. 27, no. 1, pp. 53-63, 1994.

  • T.K. Leung, M.C. Burl, and P. Perona, “Finding Faces in Cluttered Scenes Using Random Labeled Graph Matching”, Proc. Fifth IEEE Int’l Conf. Computer Vision, pp. 637-644, 1995.

  • K.C. Yow and R. Cipolla, “Feature-Based Human Face Detection”, Image and Vision Computing, vol. 15, no. 9, pp. 713-735, 1997.

  • Y. Dai and Y. Nakano, “Face-Texture Model Based on SGLD and Its Application in Face Detection in a Color Scene”, Pattern Recognition, vol. 29, no. 6, pp. 1007-1017, 1996

  • J. Yang and A. Waibel, “A Real-Time Face Tracker”, Proc. Third Workshop Applications of Computer Vision, pp. 142-147, 1996.

  • S. McKenna, S. Gong, and Y. Raja, “Modelling Facial Colour and Identity with Gaussian Mixtures”, Pattern Recognition, vol. 31, no. 12, pp. 1883-1892, 1998.

  • R. Kjeldsen and J. Kender, “Finding Skin in Color Images”, Proc. Second Int’l Conf. Automatic Face and Gesture Recognition, pp. 312-317, 1996.

  • I. Craw, D. Tock, and A. Bennett, “Finding Face Features”, Proc. Second European Conf. Computer Vision, pp. 92-96, 1992.

  • A. Lanitis, C.J. Taylor, and T.F. Cootes, “An Automatic Face Identification System Using Flexible Appearance Models”, Image and Vision Computing, vol. 13, no. 5, pp. 393-401, 1995.

  • M. Turk and A. Pentland, “Eigenfaces for Recognition”, J. Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.

  • K.-K. Sung and T. Poggio, “Example-Based Learning for View-Based Human Face Detection”, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 1, pp. 39-51, Jan. 1998.

  • Vandana S.Bhat, Dr. J. D. Pujari “Techniques for Face Detection & Recognition System: Comprehensive Review”, IOSR Journal of Computer Engineering (IOSR-JCE)

  • E. Osuna, R. Freund, and F. Girosi, “Training Support Vector Machines: An Application to Face Detection”, Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 130-136, 1997.

  • H. Schneiderman and T. Kanade, “Probabilistic Modeling of Local Appearance and Spatial Relationships for Object Recognition”, Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 45-51, 1998.

  • A. Rajagopalan, K. Kumar, J. Karlekar, R. Manivasakan, M. Patil, U. Desai, P. Poonacha, and S. Chaudhuri, “Finding Faces in Photographs”, Proc. Sixth IEEE Int’l Conf. Computer Vision, pp. 640- 645, 1998.

  • M.S. Lew, “Information Theoretic View-Based and Modular Face Detection”, Proc. Second Int’l Conf. Automatic Face and Gesture Recognition, pp. 198-203, 1996.

  • A.J. Colmenarez and T.S. Huang, “Face Detection with Information-Based Maximum Discrimination”, Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 782-787, 1997.

  • S. Gong, S. J. McKenna, and A. Psarrou, “Dynamic Vision: From Images to Face Recognition”, Imperial College Press (World Scientific Publishing Company), 2000.

  • T. Jebara,"3D Pose Estimation and Normalization for Face Recognition", Center for Intelligent Machines, McGill University, Undergraduate Thesis May

  • P. N. Belhunmeur, J. P. Hespanha, D. J. Kriegman, “Eigenfaces vs Fisherfaces Recognition Using Class specific linear projection”, IEEE Trans Pattern Analysis and Machine Intelligence, 1997.

  • J. Mazanec, M. Melisek, M. Oravec, J. Pavlovicova, “Support Vector Machine, PCA and LDA in Face Recognition”, Journal of Electrical Engineering, vol. 59, No. 4, pp. 203-209, 2008.


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  • Existing Methods for Face Detection:A Review

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Authors

Kirti P. Shinde
SITRC, Nashik, India
S. T. Gandhe
SITRC, Nashik, India

Abstract


Face detection and recognition owned significant consideration and appreciated as one of the most promising applications in the field of image processing. Face detection can consider a substantial part of face recognition operations The method of face detection in pictures is complicated because of variability present across human faces such as pose, expression, position and orientation, skin color, the presence of glasses or facial hair, differences in camera gain, lighting conditions, and image resolution. In this paper, various face detection algorithms are discussed which are frequently used. The Viola-Jones face detector is first studied. After that we survey number of techniques according to how they extract features and what learning algorithms are implemented.


Keywords


Face Detection, Classifier, Viola-Jones Face Detector, Local Binary Pattern, Hidden Markov Model, Support Vector Machine, Principal Component Analysis, Neural Network-Based Face Detection.

References