Because of the increasing instances of identity
theft and terrorism incidences in past few years, biometrics
based security system has been an area of quality research.
Modern day biometrics is a cutting edge technology which
enables the automated system to distinguish between a
genuine person and an imposter. Automated face
recognition is one of the areas of biometrics which is widely
used because of the uniqueness of one human face to other
human face. Automated face recognition has basically two
parts one is face detection and other one is recognition of
detected faces. To detect a face from an online surveillance
system or an offline image, the main component that should
be detected is the skin areas. This paper proposes a skin
based segmentation algorithm for face detection in color
images with detection of multiple faces and skin regions.
Skin color has proven to be a useful and robust cue for face
detection, localization and tracking.
Ms. Kanchan Wani : had completed her BE in E&TC and
pursuing M.E. from the J.T. Mahajan college of Enginerering
Faizpur. Maharashtra.
Mr. S. V. Patil : M.E.(Control & Instrumentation) working as a Sr.
Lecturer in Dept of E&TC J.T. Mahajan College of Engineering,
Faizpur. Maharashtra.
HSV
YCrCb and RGB
SVM
Mostly in the criminal justice application facial
recognition provides an alternative method to make sure
that the databases do not contain multiple records for a
single individual. Thus, it allows people to be identified when it is not possible to take the fingerprints for
physical or legal reasons.
2. IDs checks can be carried out on just the faces or the
fingerprints; the combination of the two biometric
techniques increases the accuracy of the searches and
allows reliable decisions to be sent to the required field.
Even, if the quality of the external facial images is highly
variable, it is possible to compare them with the
photograph of the person known to the Police.
[1] Eric Hess ‘’Facial recognition: A valuable tool for
Law Enforcement’’, Forensic magazine, Vol.7/ No.5,
November 2010.
[2] S.Ravi, S.Wilson, “Face detection with facial features
and gender classification based on support vector
machine”, 2010 IEEE International Conference on
Computational Intelligence and computing Research,
ISBN: 97881 8371 362 7.
[3] Michael Padilla and Zihong Fan, “ EE368 Digital
Image Processing Project- Automatic Face Detection
using Color Based Segmentation and
Template/Energy Thresholding’’, Department of
Electrical engineering, EE368, Stanford University.
[4] Zhao, W., Chellappa, R., Rosenfeld, A and Phillips,
P.J., 2003, “Face Recognition: A Literature Survey”,
ACM Computing Surveys.
[5] Mun Wai Lee and Ramakant Nevatia, January 2009,
“Human Pose Tracking in Monocular Sequence
Using Multilevel Structured Models”, IEEE
Transactions on Pattern Analysis and Machine
Intelligence, Vol. 31, No.1, pp.27-38.
[6] Aysegul Gunduz and Hamid Krim, 2003, “Facial
Feature Extraction Using Topological Methods”,
IEEE International Conference on Image Processing.
[7] Lekshmi, V. Praseeda and Sasikumar, M., 2009,
“Facial Expression Recognition from Global and a
Combination of Local Features”, IETE Technical
Review, Vol 26, No.1, pp. 41-46.
[8] Jinchang Ren and Jianmin Jiang, 2007 “Statistical
Classification of Skin Color Pixels from MPEG
Videos”, Springer Link.
[9] Christophe Laurent, Nathalie Laurent, Yann Bodo,
2003, “A Human Skin Detector combining Mean
Shift Analysis and Watershed Algorithm”, IEEE
Proceedings of the International Conference on
Image Processing.
[10] Busin, L, Shi, J and Macaire, L., 2008, “Color space
selection for color image segmentation by spectral
clustering”, LFA Summary.
[11] Jun Zhang, Qieshi Zhang and Jinglu Hu, 2009, “RGB
Color Centroids Segmentation(CCS) for Face
Detection”, ICGST-GVIP Journal, ISSN 1687-398X,
Vol.9, No.2, pp.1-9.
[12] Rafal Kozik and Michal Choras, 2010, “Combined
Shape and Texture Information for Palmprint
Biometrics”, Journal of Information Assurance and
Security, Vol.5, No.1, pp.58-63. [13] Ravichandran, K.S. and Ananthi, B., 2009, “Color
Skin Segmentation Using K-Means Cluster”,
International Journal of Computational and Applied
Mathematics, Vol.4, No.2, pp.153–157.
[14] Rosalyn R. Porle, Ali Chekima, Farrah Wong, and G.
Sainarayanan, 2009, “Performance of Histogram-
Based.
