Face Recognition using SIFT, SURF and PCA for Invariant Faces

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2016 by IJETT Journal
Volume-34 Number-1
Year of Publication : 2016
Authors : Yukti Bakhshi, Sukhvir Kaur, Prince Verma


Yukti Bakhshi, Sukhvir Kaur, Prince Verma"Face Recognition using SIFT, SURF and PCA for Invariant Faces", International Journal of Engineering Trends and Technology (IJETT), V34(1),39-42 April 2016. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

This paper consists of review methods used for face recognition- SIFT, SURF, PCA, PCASIFT, etc. for recognition and matching. SIFT and SURF is used to extract features to perform reliable matching from the images. PCA eigenfaces are used, they are entered into SIFT. The basic process of face recognition system is described here and improvement is shown in matching the invariant faces in this paper. SIFT and SURF are used to extract the features and then applying PCA to the image for the better performance in terms of rotation, expression and contrast. Performance can be seen on the basis of Recognition rate. Image Processing Toolbox under MATLAB Software is used for the implementation of this proposed work.


[1] Dong Li, Huiling Zhou and Kin-Man Lam, “High Resolution Face Verification Using Pore-Scale Facial Features”, IEEE transactions on image processing, vol. 24, no. 8, pp. 2317-2327, 2015.
[2] Priyanka, Dr. Yashpal Singh, “A Study on Facial Feature Extraction and Facial Recognition Approaches”, International Journal of Computer Science and Mobile Computing, vol. 4, pp. 166-174, 2014.
[3] Vrushali Purandare and KT Talele, “Efficient Heterogeneous Face Recognition using Scale Invariant Feature Transform”, IEEE International Conference on Circuits, Systems, Communication and Information Technology Applications (CSCITA), vol. 1, pp. 305-310, 2014.
[4] Vikram Solunke, Pratik Kudle, Abhijit Bhise, Adil Naik and Prof. J.R. Prasad, “A Comparison between Feature Extraction Techniques for Face Recognition”, International Journal of Emerging Research in Management & Technology, vol. 3, pp. 38-41, 2014.
[5] Isra’a Abdul-Ameer Abdul-Jabbar, Jieqing Tan and Zhengfeng Hou, “Adaptive PCA-SIFT Matching Approach for Face Recognition Algorithms”, International MultiConference of Engineers and Computer Scientists (IMECS), vol. I., 2014.
[6] Sapna Vishwakarma, . Krishan Kant Pathak, “Face recognition using LBP Coefficient Vectors with SVM Classifier”, International Journal of Engineering Trends and Technology (IJETT), vol. 9, no. 2, pp. 96-100, 2014.
[7] B.K. Bairagi, S.C. Das, A. Chatterjee, B. Tudu, “Expressions invariant face recognition using SURF and Gabor features”, IEEE Third International Conference on Emerging Applications of Information Technology, pp. 170-173, 2012.
[8] Shungang Hua, Guopeng Chen, Honglei Wei and Qiuxin Jiang, “Similarity measure for image resizing using SIFT feature”, EURASIP Journal on Image and Video Processing, SPRINGER, no. 1, 2012.
[9] A. Bansal, K. Mehta, S. Arora, “Face Recognition using PCA Algorithm and LDA”, IEEE Second International Conference on Advanced Computing and Communication Technologies, pp. 251-254, 2012.
[10] Shinfeng D. Lin, Bo-Feng Liu and Jia Hong Lin, “Combining Speeded-Up Robust Features with Principal Component Analysis in Face Recognition System”, International Journal of Innovative Computing, Information and Control, vol. 8, pp. 8545-8556, 2012.
[11] Aruni Singh, Sanjay Kumar Singh, Shrikant Tiwar, “Comparison of face Recognition Algorithms on Dummy Faces”, The International Journal of Multimedia & Its Applications (IJMA) vol.4, pp. 121-135, 2012.
[12] Ergun Gumus, Niyazi Kilic, Ahmet Sertbas, Osman N. Ucan, “Evaluation of face recognition techniques using PCA, wavelets and SVM”, Elsevier Expert Systems with Applications, vol. 37, pp. 6404-6408, 2010.
[13] T.F. Karim, M.S.H. Lipu, M.L. Rahman, F. Sultana, “Face Recognition using PCA based method”, IEEE International Conference on Advanced Management Science, pp. 158- 162, 2010.
[14] Geng Du, Fei Su, Anni Cai, “Face Recognition using SURF features”, Pattern Recognition and Computer Vision, vol. 7496, 2009.
[15] Herbert Bay, Andreas Ess, Tinne Tuytelaars and Luc Van Gool, “Speeded-Up Robust Features (SURF)”, Elsevier Computer Vision and Image Understanding, vol. 110, pp. 346-359, 2008.
[16] Jun Luo, Yong Ma, Erina Takikawa, Shihong Lao, Masato Kawade, Bao-Liang Lu, “Person specific SIFT features for face recognition”, IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 593-596, 2007.
[17] David G. Lowe, “Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision, 2004.
[18] Yan Ke, R. Sukthankar, “PCA-SIFT: A more distinctive representation for local image descriptors”, Computer Vision and Pattern Recognition, CVPR, IEEE, vol. 2, pp. 506-513, 2004.
[19] Yukti Bakhshi, Sukhvir Kaur, Prince Verma, “A Study based on various Face Recognition Algorithms”, International Journal of Computer Applications (IJCA), vol. 129, no. 13, pp. 16-20, 2015.

Face Recognition, Face Recognition Algorithms, SIFT, SURF and PCA, Recognition Rate.