FACE RECOGNITION WITH ILLUMINATION VARYING CONDITIONS AND OCCLUSIONS
DOI:
https://doi.org/10.20319/mijst.2016.s21.150166Keywords:
Face Recognition, Illumination Normalization and Occlusion, Small Sample Size.Abstract
Face recognition with illumination varying conditions and occlusions is one of the most important challenges in the field of digital image processing. Despite of the fact that a number of studies (Patel & Yagni, 2013; Azeem, Sharif, Raza & Murtaza, 2014) have improved the accuracy of different techniques by normalizing and compensating the illumination variations using some pre-processing methods, a lot of these methods are still facing many serious challenges with illumination changes and occlusion. In this paper, we suggest the use of tow pre-processing methods that will have a great impact on the performance and the robustness of the recognition procedures in case small sample size (SSS) as the training set.
References
Anila, S &devarajan, N. (2012). Preprocessing Technique for Face Recognition Applica-tions under Varying Illumination Conditions. Global Journal of Computer Science and Technology Graphics & Vision, 12(11).
Azeem et al.. (2014). A Survey: Face Recognition Techniques under Partial Occlusion. In-ternational Arab Journal of Information Technology, 11(1).
Dharavath, K, Talukdar, F.A &Laskar, R.H. (2014). Improving Face Recognition Rate with Image Preprocessing. Indian Journal of Science and Technology.
Fidler,S, Leonardis,A. (2003). Robust LDA Classification by Subsampling. Computer Vi-sion and Pattern Recognition Workshop, 8(1063-6919).
Forczmanski, P.; Kukharev, G.; Shchegoleva, N.(2012).An algorithm of face recognition under difficult lighting conditions. Electrical Review, No. 10b, pp. 201-204, 2012.
Han et al..(2012). A comparative study on illumination preprocessing in face recognition. Journal Of the Pattern Recognition society, 46(6).
Lee, K.C, Ho, J &Kriegman, D. (2005). Acquiring Linear Subspaces for Face Recognition under Variable Lighting. IEEE Transactions On Pattern Analysis And Machine In-telligence, 27(5).
Mokhtare, N &Faez, K. (2016). Face Recognition under Local Occlusion Conditions and Varying Illumination. International Journal of Mechatronics,Electrical and Com-puter Technology, 6(20).
Patel, R & yagnik, S.B. (2013). A Literature Survey on Face Recognition Techniques. In ternational Journal of Computer Trends and Technology , 5(4).
Pratt, W. K., Digital Image Processing(2001).
Sahu, K, Singh, Y.P &Kulshrestha, A. (2013). A Comparative Study of Face Recognition System Using PCA and LDA.International Journal of IT, Engineering and Applied Sciences Research, 2(10).
Struc, V, Zibert, J &Pavesic, N. (2009). Histogram Remapping as a Preprocessing Step for Robust FaceRecognition. WSEAS Transactions on Information Science and Appli-cations, 6(3).
Tan et al..(2005). Recognizing Partially Occluded, Expression Variant Faces from Single Training Image per Person with SOM and soft kNN Ensemble. IEEE Transactions on Neural Networks, 16(4) .
Wang, X &Paliwal, K.K. (2003). Feature extraction and dimensionality reduction algo-rithms and their applications in vowel recognition. Journal Of the Pattern Recogni-tion society, 36(10).
Wright et al..(2009). Robust Face Recognition via Sparse Representation. IEEE Transac-tions On Pattern Analysis And Machine Intelligence, 31(2).
Yang M，Feng Z Z，Shiu S C..( 2014)． Fast and Robust Face Recognition via Coding Residual Map Learning Based Adaptive Masking Journal of Pattern Ｒecognition，47( 2)．
Zhao et al..(2015). A Wavelet-Based Image Preprocessing Method for Illumination Insen-sitive Face Recognition. Journal of Information Science & Engineering, 31(5) .
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2016 Authors
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright of Published Articles
Author(s) retain the article copyright and publishing rights without any restrictions.
All published work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
