The week's pick
Random Articles
Reseach Article
MEN: Multi-Attribute Feature Selection for Face Recognition
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 93 - Number 1 |
| Year of Publication: 2014 |
| Authors: P. Ramaraj, M. Prabakaran |
10.5120/16178-4469
|
P. Ramaraj, M. Prabakaran . MEN: Multi-Attribute Feature Selection for Face Recognition. International Journal of Computer Applications. 93, 1 ( May 2014), 12-16. DOI=10.5120/16178-4469
Abstract
Face recognition has become more sophisticated in biometric based authentication systems, which uses various facial features. The authentication mechanism is still required with more features to be used and has to be done in a short time. Existing face recognition algorithms are more scalable in time and memory used, also produces high frequency of false positive results. To overcome the problem of false positive results, we propose MEN-Mouth, Eye and Nose features based multi attribute feature selection method for face recognition. The proposed method extracts the facial features like Nose, mouth and eye, from extracted features we compute eccentric measures for each of the feature. The eccentric measure is computed between four axis co-ordinates of facial features. Computed features are converted into single feature, and computes feature weight based on computed feature set. The computed feature weight is used to recognize the person.
References
- TahiaFahrinKarim, Md. LushanurRahman, Molla ShahadatHossain Lipu and Faria Sultana, "Face Recognition using PCA-Based Method", IEEE International Conference on AdvancedManagement Science , vol. 3, pp. 158- 162, 2010.
- GopaBhaumik, TanwiMallick, Koyel Sinha Chowdhury, GautamSanyal, "Analysis andDetection of Human Faces by using Minimum Distance Classifier for Surveillance", IEEEInternational Conference on Recent Trends in Information, Telecommunication and Computing, pp. 265-267, 2010.
- Gopa Bhaumik, Tanwi Mallick, Koyel Sinha Chowdhury, GautamSanyal, "Analysis andDetection of Human Faces by using Minimum Distance Classifier for Surveillance", IEEEInternational Conference on Recent Trends in Information, Telecommunication and Computing, pp. 265-267, 2010.
- S Sumathi and R Rani HemaMalini,"Face Recognition System to enhance E-Health," IEEEInternational Conference on E-Health Networking, Digital Ecosystems and Technologies, pp. 195-198, 2010.
- Haijun Zhang, Q. M. JonathanWu, TommyW. S. Chow, and MingboZhao, "A two-dimensionalNeighborhood Preserving Projection for appearance-based face recognition," ELSEVIERInternational Journal on Pattern Recognition, Vol. 45, Issue 5, pp. 1866-1876,2012.
- Loris Nanni, Alessandra Lumini, and Sheryl Brahnam, "Survey on LBP based texturedescriptors for image classification," ELSEVIER International Journal on Expert Systems withApplications, Vol. 39, Issue 3, pp. 3634-364, 2012.
- Francisco A. Pujol and Juan Carlos García, "Computing the Principal Local Binary Patternsfor face recognition using data mining tools," ELSEVIER International Journal on Expert Systemswith Applications, Article in press,2012.
- T. Barbu, An Automatic Face Detection System for RGB Images, Int. J. of Computers, Communications & Control,2011.
- Jagadeesh H S1, Suresh Babu K2, and Raja K B2 , DBC based Face Recognition using DWT, Signal & Image Processing : An International Journal (SIPIJ) Vol. 3, No. 2, April 2012
Index Terms
Keywords