Robust Face Recognition Under Illumination Changes and Pose Variations

Document Type: Original Article


1 Prof ., Faculty of Computer Engineering & IT, Shahrood University of Technology, Shahrood, Iran

2 Faculty of Computer Engineering & IT, Shahrood University of Technology, Prof. Shahrood, Iran

3 Assistant professor, Facaulty of Technology and Engineering, University of Mazandaran, Babolsar, Iran


There are many applications for face recognition. Due to illumination changes, and pose variations of facial images, face recognition is often a challenging and a complicated process. In this paper, we propose an effective and robust face recognition method. Firstly, we select those areas from the face (such as eyes, nose, and mouth), which are more informative in face recognition. Then SIFT (Scale Invariant Feature Transform) descriptor is utilized for feature extraction from the selected areas. SIFT descriptor detects keypoints in the image and describes each keypoint with a feature vector with length 128. To speed up the proposed method, PCA (Principal Component Analysis) is applied on the SIFT feature vector to reduce the vector’s length. Finally, Kepenekci matching method is used to assess similarity between the images. The proposed method is evaluated on the ORL, Extended Yale B, and FEI databases. Results show considerable performance of the proposed face recognition method in comparison with several state-of-the-arts.


[1] R. Min, A. Hadid, and J. L. Dugelay. Improving the recognition of faces occluded by facial accessories. In IEEE International Conference on Automatic Face & Gesture Recognition and Workshop, pages 442--447. IEEE, 2011. [ bib | DOI ]
[2] L. Lenc and P. Krl. A combined SIFT/SURF descriptor for automatic face recognition. In Proceedings of SPIE - The International Society for Optical Engineering, 2013. [ bib | DOI ]
[3] D.G. Lowe. Object recognition from local scale-invariant features. In Proceedings of the Seventh IEEE International Conference on Computer Vision, pages 1150--1157. IEEE, 1999. [ bib | DOI ]
[4] Janez Kri?aj, Vitomir ?truc, and Nikola Pave?i? Adaptation of SIFT Features for Robust Face Recognition. In International Conference Image Analysis and Recognition, pages 394--404. Springer, Berlin, Heidelberg, 2010. [ bib | DOI ]
[5] M. Turk and A. Pentland. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1):71--86, 1991. [ bib | DOI ]
[6] P.N. Belhumeur, J.P. Hespanha, and D.J. Kriegman. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):711 -- 720, 1997. [ bib | DOI ]
[7] M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski. Face recognition by independent component analysis. IEEE Transactions on Neural Networks, 13(6):1450–1464, 2002. [ bib | DOI ]
[8] R. Brunelli and T. Poggio. Face recognition through geometrical features. In European Conference on Computer Vision, pages 792--800. Springer, Berlin, Heidelberg, 1992. [ bib | DOI ]
[9] T. Ahonen, A. Hadid, and M. Pietik?inen. Face Recognition with Local Binary Patterns. In European Conference on Computer Vision, pages 469--481. Springer, Berlin, Heidelberg, 2004. [ bib | DOI ]
[10] B. V. Kumar and B. S. Shreyas. Face recognition using gabor wavelets. In 2006 Fortieth Asilomar Conference on Signals, Systems and Computers, pages 593--597. IEEE, 2006. [ bib | DOI ]
[11] David G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91–110, 2004. [ bib | DOI ]
[12] H. Bay, T. Tuytelaars, and L. V. Gool. Surf: Speeded up robust features. In European Conference on Computer Vision, pages 404--417. Springer, Berlin, Heidelberg, 2006. [ bib | DOI ]
[13] M.V. Sudhamani and K. Halappa. Experimental analysis of SIFT and SURF features for multi-object image retrieval. International Journal of Computational Vision and Robotics, 7(3):344--356, 2017. [ bib | DOI ]
[14] V. Devi, J. Baber, M. Bakhtyar, I. Ullah, W. Noor, and A. Basit. Performance Evaluation of SIFT and Convolutional Neural Network for Image Retrieval. International Journal of Advanced Computer Science and Applications, 8(12), 2017. [ bib | DOI ]
[15] H. Guo, S. Su, J. Liu, Z. Sun, and Y. Xu. An Image Retrieval Method Based on Manifold Learning with Scale-Invariant Feature Control. Telecommunication Computing Electronics and Control, 17(2):252--258, 2016. [ bib | DOI ]
[16] V. Purandare and K. T. Talele. Efficient heterogeneous face recognition using Scale Invariant Feature Transform. In International Conference on Circuits, Systems, Communication and Information Technology Applications (CSCITA), pages 305--310. IEEE, 2014. [ bib | DOI ]
[17] H. Kumar and P. Padmavati. Face Recognition using SIFT by varying Distance Calculation Matching Method. International Journal of Computer Applications, 47(3):20--26, 2012. [ bib | DOI ]
[18] L. Lenc and P. Kr?l. Novel matching methods for automatic face recognition using sift. In International Conference on Artificial Intelligence Applications and Innovations, pages 254--263. Springer, Berlin, Heidelberg, 2012. [ bib | DOI ]
[19] N. Sang, J. Wu, and K. Yu. Local Gabor Fisher Classi?er for Face Recognition. In Fourth International Conference on Image and Graphics (ICIG 2007), pages 620--626. IEEE, 2007. [ bib | DOI ]
[20] M. Srinivasan and N. Ravichandran and. A new technique for Face Recognition using 2D-Gabor Wavelet Transform with 2D-Hidden Markov Model approach. In International Conference on Signal Processing , Image Processing & Pattern Recognition, pages 151--156. IEEE, 2013. [ bib | DOI ]
[21] K. Lai, A. Poursaberi, and S. Yanushkevich. One-shot facial feature extraction based on Gauss-Laguerre filter. In IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE), pages 1--6. IEEE, 2014. [ bib | DOI ]
[22] I. Dagher, N. E. Sallak, and H. Hazim. Face recognition using the most representative sift images. International Journal of Signal Processing, Image Processing and Pattern Recognition, 7(1):225--236, 2014. [ bib | DOI ]
[23] W. ZHANG, X. ZHAO, J. M. Morvan, and L. Chen. Improving Shadow Suppression for Illumination Robust Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence ( Early Access), pages 1 -- 1, 2018. [ bib | DOI ]
[24] D. Chen, X. Cao, F. Wen, and J. Sun. Blessing of dimensionality: High-dimensional feature and its efficient compression for face verification. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 3025--3032. IEEE, 2013. [ bib | DOI ]
[25] C. Ding, C. Xu, and D. Tao. Multi-task Pose-Invariant Face Recognition. IEEE Transactions on Image Processing, 24(3):980 -- 993, 2015. [ bib | DOI ]
[26] W. Huang and H. Yin. Robust Face Recognition with Structural Binary Gradient Patterns. Pattern Recognition, 68:126--140, 2017. [ bib | DOI ]
[27] M. Z. N. Al-Dabagh, M. H. M. Alhabib, and F. H. AL-Mukhtar. Face Recognition System Based on Kernel Discriminant Analysis, K-Nearest Neighbor and Support Vector Machine. International Journal of Research and Engineering, 5(3):335--338, 2018. [ bib | DOI ]
[28] D. Sage. Biomedical Imaging Group., Date Accessed: August 15, 2017. [ bib ]
[29] Y. Shi, J. Yang, and R. Wu. Reducing illumination based on nonlinear gamma correction. In IEEE International Conference on Image Processing, pages 529 -- 532. IEEE, 2007. [ bib | DOI ]
[30] H. Hassanpour. Image Enhancement via Reducing Impairment Effects on Image Components. International Journal of Engineering-Transactions B: Applications, 26(11):1267--1274, 2013. [ bib | DOI ]
[31] S. A. Amir and H. Hassanpour. A Preprocessing Approach For Image Analysis Using Gamma Correction. International Journal of Computer Applications, 38(12):38--46, 2012. [ bib ]
[32] M. U., V. Franc, and V. Hlavac. Detector of facial landmarks learned by the structured output SVM. International Conference on Computer Vision Theory and Applications, 1:547--556, 2012. [ bib ]
[33] ORL Database. Digital Technology Group., Date Accessed: August 15, 2017. [ bib ]
[34] C.E. Thomaz. FEI Database., Date Accessed: August 15, 2017. [ bib ]
[35] k.Ch. Lee. Extended Yale B Database., Date Accessed: August 15, 2017. [ bib ]