Commenced in January 2007
Paper Count: 31824
Face Detection using Variance based Haar-Like feature and SVM
Abstract:This paper proposes a new approach to perform the problem of real-time face detection. The proposed method combines primitive Haar-Like feature and variance value to construct a new feature, so-called Variance based Haar-Like feature. Face in image can be represented with a small quantity of features using this new feature. We used SVM instead of AdaBoost for training and classification. We made a database containing 5,000 face samples and 10,000 non-face samples extracted from real images for learning purposed. The 5,000 face samples contain many images which have many differences of light conditions. And experiments showed that face detection system using Variance based Haar-Like feature and SVM can be much more efficient than face detection system using primitive Haar-Like feature and AdaBoost. We tested our method on two Face databases and one Non-Face database. We have obtained 96.17% of correct detection rate on YaleB face database, which is higher 4.21% than that of using primitive Haar-Like feature and AdaBoost.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1060471Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3543
 Tan. H, Chen. H, Selecting Frequency Feature for License Plate Detection Based on AdaBoost, Proceedings of SPIE-IS and T Electronic Imaging- Visual Communications and Image Processing
 Negri. P, Clady. X, Prevost. L, Benchmarking Haar and Histograms of Oriented Gradients features applied to vehicle detection, Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics, 2007
 Bai. H, Wu. J, Liu. C, Motion and haar-like features based vehicle detection, Multi-Media Modelling Conference Proceedings, 2006 12th International
 Stanciulescu. B, Breheret. A, Moutarde. F, Introducing New AdaBoost Features for Real-Time Vehicle Detection, Proceedings of COGIS-07 conference on COGnitive systems with Interactive Sensors, held in Stanford University California, Nov 2007
 Haselhoff. A, Kummert. A, Schneider. G, Radar-Vision Fusion with an Application to Car-Following using an Improved AdaBoost Detection Algorithm, Intelligent Transportation Systems Conference, ITSC 2007, IEEE, pages: 854-858, Sept 2007
 Lausser. L, Schwenker. F, Palm. G, Detecting zebra crossings utilizing AdaBoost, 16th European Symposium on Artificial Neural Networks, Bruges, Belgium, April 23-25, 2008
 Yoon. C, Cheon. M, Kim. E, Park. M, Lee. H, Real-time road sign detection using Adaboost and Multicandidate, 2007 International Symposium on Advanced Intelligent Systems, Sokcho, Korea
 Nishimura. J, Kuroda. T, Low cost speech detection using Haar-like filtering for sensornet, Signal Processing, 2008. ICSP 2008. 9th International Conference, pages: 2608-2611, Oct. 2008
 Viola. P, Jones. M, Rapid object detection using a boosted cascade of simple features, Proceedings of the 2001 IEEE Computer Society Conference, vol 1, pages: 511-518, 2001
 Viola. P, Jones. M, Robust Real-time Object Detection, Second international workshop on statistical and computational theories of vision - modeling, learning, computing, and sampling. Vancouver, Canada, July 13, 2001
 Viola. P, Jones. M, Robust real-time face detection, International Journal of Computer Vision 57(2), 137-154, 2004, vol 2, pages: 747-747, July 2001
 Paisitkriangkrai. S, Shen. C, Zhang. J, Fast Pedestrian Detection Using a Cascade of Boosted Covariance Features, Circuits and Systems for Video Technology, IEEE Transactions, vol 18, pages: 1140-1151, Aug 2008
 Feng Tang, Crabb. R, Hai Tao, Representing Images Using Nonorthogonal Haar-Like Bases, Pattern Analysis and Machine Intelligence, IEEE Transactions, vol 29, pages: 2120-2134, Dec. 2007
 Chen. Q, Georganas. N. D, Petriu. E. M, Hand Gesture Recognition Using Haar-Like Features and a Stochastic Context-Free Grammar, Instrumentation and Measurement, IEEE Transactions, vol 57, pages: 1562-1571, Aug 2008
 Sonka. M,Hlavac. V, Boyle. R, Image Processing, Analysis, and Machine Vision. Boston, MA: PWS-Kent, 1999.
 Andrew Webb, Statistical Pattern Recognition, Oxford University Press, New York, 1999.