Face Detection via Bayesian Model with Homogeneity Property

H. S. Shukla; Ravi Verma

doi:https://doi.org/10.51976/ijari.321515

Submit Manuscript Login / Register Subscribe

Home Page

Editorial Advisory Board

Mission, Aims & Scope

Current Issue

Face Detection via Bayesian Model with Homogeneity Property

H. S. Shukla, Ravi Verma

Vol 3 , Issue 2 , April - June 2015 | Pages: 63-67 | Research Paper

https://doi.org/10.51976/ijari.321515

Received: April 05, 2015 | Revised: April 30, 2015 | Accepted: May 20, 2015 | Published Online: June 15, 2015

Author Details ( * ) denotes Corresponding author

1. * H. S. Shukla, Department of Computer Science, Deen Dayal Upadhaya Gorakhpur University, Gorakhpur, Uttar Pradesh, India (forever_ravi123@yahoo.co.in)

2. Ravi Verma, Department of Computer Science, Deen Dayal Upadhaya Gorakhpur University, Gorakhpur, Uttar Pradesh, India

In this paper, a calculation for dividing skin districts in shading pictures utilizing shading and edge data is exhibited. Skin hued areas are initially recognized utilizing a Bayesian model of the human skin shading. These areas are further divided into skin district competitors that fulfill the homogeneity property of the human skin. We demonstrate that Bayesian skin shading model beats numerous different models, for example, the piece-wise straight models, Gaussian models and model in light of multilayer discernments. Test results show that the proposed division calculation decreases false location brought about by foundation pixels having skin hues, and all the more altogether it is fit for isolating genuine skin districts from dishonestly recognized areas.

Keywords

Bayesian Model; Segmentation regions; Face detection and Homogeneity Property

2. M-H Yang, D. Kriegman, N. Ahuja, Detecting Face in Images: A Survey, IEEE Transaction on Pattern Analysis and Machine Intelligence, (24)1, 2002, 34-58
4. E. Hjelms, B-K Low, Face Detection: A Survey, Computer Vision and Image Understanding, 83, 2001, 236-274
6. H. Rowley, S. Baluja, T. Kanade, Neural network-based face detection, IEEE Trans on PAMI, 20(1), 1998, 23-38
8. E. Osuna, R. Freund, F. Girosi, Training support vector machines: an application to face detection, IEEE CVPR, 1997, 130-136
10. T. Leung, M. C. Burl, P Perona, Finding faces in cluttered scenes using random labelled graph matching, ICCV, 1995
12. P. Viola, M. Jones, Robust real-time object detection, Second International Workshop on Statistical and Computational Theories of Vision-Modeling, Learning, Computing and Sampling, 2001
14. F. Fleuret, D. Geman, Coarse-to-fine visual selection, IJCV, 41(2), 2001
16. H. P. Graf, T. Chen, E. Petajan, E. Cosatto, Locating Faces and Facial Parts, Proc. First Int’l Workshop Automatic Face and Gesture Recognition, 1995, 41-46
18. H. P. Graf, E. Cosatto, D. Gibbon, M. Kocheisen, E. Petajan, Multimodal System for Locating Heads and Faces, Second Int’l Conf. Automatic Face and Gesture Recognition, 1996, 88-93
20. H. Martin Hunke, Locating and tracking of human faces with neural networks, Master’s thesis, University of Karlsruhe, 1994
22. John Hertz, Anders Krogh, Richard G. Palmer, Introduction to the Theory of Neural Computation, Addision-Wesley Publishing Company, Reading, Massachusetts, 1991
24. Tom M. Mitchell, Machine Learning, McGraw-Hill, 1997
26. http://www.ai.mit.edu/projects/cbcl/software-datasets/FaceData2.h