Face Recognition using SIFT, SURF and PCA for Invariant Faces

This paper consists of review methods used for face recognitionSIFT, 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]  Shinfeng D. Lin,et al.  COMBINING SPEEDED-UP ROBUST FEATURES WITH PRINCIPAL COMPONENT ANALYSIS IN FACE RECOGNITION SYSTEM , 2012 .

[2]  Sapna Vishwakarma,et al.  Face Recognition using LBP Coefficient Vectors with SVM Classifier , 2014 .

[3]  Fei Su,et al.  Face recognition using SURF features , 2009, International Symposium on Multispectral Image Processing and Pattern Recognition.

[4]  B. Tudu,et al.  Expressions invariant face recognition using SURF and Gabor features , 2012, 2012 Third International Conference on Emerging Applications of Information Technology.

[5]  Shungang Hua,et al.  Similarity measure for image resizing using SIFT feature , 2012, EURASIP J. Image Video Process..

[6]  Satish K. Singh,et al.  Comparison of face Recognition Algorithms on Dummy Faces , 2012 .

[7]  Sanjeev Dhawan,et al.  Feature Extraction Techniques for Face Recognition , 2012 .

[8]  Prince Verma,et al.  A Study based on Various Face Recognition Algorithms , 2015 .

[9]  Kin-Man Lam,et al.  High-Resolution Face Verification Using Pore-Scale Facial Features , 2015, IEEE Transactions on Image Processing.

[10]  Yan Ke,et al.  PCA-SIFT: a more distinctive representation for local image descriptors , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[11]  T. F. Karim,et al.  Face recognition using PCA-based method , 2010, 2010 IEEE International Conference on Advanced Management Science(ICAMS 2010).

[12]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[13]  K. T. Talele,et al.  Efficient heterogeneous face recognition using Scale Invariant Feature Transform , 2014, 2014 International Conference on Circuits, Systems, Communication and Information Technology Applications (CSCITA).

[14]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[15]  Ahmet Sertbas,et al.  Evaluation of face recognition techniques using PCA, wavelets and SVM , 2010, Expert Syst. Appl..

[16]  Jun Luo,et al.  Person-Specific SIFT Features for Face Recognition , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.