PSI (ψ) Invariant Features for Face Recognition

  • Ajaykumar S. CholinEmail author
  • A. Vinay
  • Aditya D. Bhat
  • Arnav Ajay Deshpande
  • K. N. B. Murthy
  • S. Natarajan
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 308)


Over last few decades, mathematics has played a crucial role in developing efficient algorithms for Face Recognition (FR) used in biometric systems. FR using Machine Learning (ML) techniques has impacted FR systems tremendously, towards efficient and accurate models for FR. Existing FR systems used in biometrics use ML techniques to learn patterns in the images by extracting various features from them and often require pre-processed face image data for the learning process. In this paper, we have used various pre-processing techniques and compared them in the deployed FR framework. It was observed that the Steerable Pyramid (SP) filter was the most efficient pre-processing technique among all techniques used for pre-processing in this work. Though existing feature extraction methods such as SIFT (Scale-Invariant Feature Transform), SURF (Speeded-Up Robust Features), ORB (Oriented FAST and Rotated BRIEF) have been used in the past, they have not been accurate enough in various vision based biometric systems. Hence, a novel PSI (Pose Scale and Illumination) invariant SURF-RootSIFT technique is proposed by extending the well known SIFT-RootSIFT feature extraction technique which is achieved by calculating the Bhattacharya Coefficient between the feature vectors. A framework which uses the proposed novel feature extraction technique is deployed in this work. This paper demonstrates that the novel SURF-RootSIFT based framework is proven to perform more accurately and efficiently than the other techniques, with 99.65, 99.74 and 97.93% accuracy on the Grimace, Faces95 and Faces96 databases respectively.


Face recognition Image pre-processing SIFT SURF RootSIFT Bhattacharya coefficient VLAD 


  1. 1.
    Perlibakas, V.: Distance measures for PCA-based face recognition. Pattern Recog. Lett. 25(6), 711–724 (2004). ISSN 0167-8655CrossRefGoogle Scholar
  2. 2.
    Mudrová, M., Procházka, A.: Principal component analysis in image processing (2018)Google Scholar
  3. 3.
    Chen, L.F., Liao, H.Y., Ko, M.T., Lin, J.C., Yu, G.J.: New LDA-based face recognition system which can solve the small sample size problem. Pattern Recogn. 33, 1713–1726 (2000). Scholar
  4. 4.
    Yang, J., Zhang, D., Yong, X., Yang, J.Y.: Two-dimensional discriminant transform for face recognition. Pattern Recog. 38(7), 1125–1129 (2005). ISSN 0031-3203CrossRefGoogle Scholar
  5. 5.
    Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. In: Zhou, S.K., Zhao, W., Tang, X., Gong, S. (eds.) Analysis and Modeling of Faces and Gestures. AMFG 2007. Lecture Notes in Computer Science, vol. 4778. Springer, Berlin, Heidelberg (2007)Google Scholar
  6. 6.
    He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.J.: Face recognition using laplacian faces. IEEE Trans. Pattern Anal. Mach. Intell. 27, 328–340 (2005). Scholar
  7. 7.
    Simoncelli, E.P., Freeman, W.T.: The steerable pyramid: a flexible architecture for multi-scale derivative computation. In ICIP, p. 3444 (1995)Google Scholar
  8. 8.
    Adak, C.: Gabor filter and rough clustering based edge detection. In: 2013 International Conference on Human Computer Interactions (ICHCI), Chennai, pp. 1–5 (2013)Google Scholar
  9. 9.
    Chua, L., Yang, L.: Cellular neural networks: theory. Circuits Syst. IEEE Trans. 35, 1257–1272 (1988). Scholar
  10. 10.
    Chua, L.O., Yang, L.: Cellular neural networks: applications. IEEE Trans. Circuits Syst. 35(10), 1273–1290 (1988)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  12. 12.
    Jurie, F., Schmid, C.: Scale-invariant shape features for recognition of object categories. In: CVPR, vol. 2, pp. 90–96 (2004)Google Scholar
  13. 13.
    Geng, C., Jiang, X.: Face recognition using sift features. In Image Processing (ICIP), 2009 16th IEEE International Conference on, IEEE, pp. 3313–3316 (2009)Google Scholar
  14. 14.
    Bay, H., Tuytelaars, T., Van Gool, L.: Surf: Speeded up robust features. In the proceedings of European Conference on Computer Vision, vol. 1, Graz, Austria, pp. 404–417 (May 2006)CrossRefGoogle Scholar
  15. 15.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRefGoogle Scholar
  16. 16.
    Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: Computer Vision (ICCV), 2011 IEEE International Conference on, IEEE, pp. 2564–2571 (2011)Google Scholar
  17. 17.
    Arandjelovic, R., Zisserman, A.: Three things everyone should know to improve object retrieval (2012)Google Scholar
  18. 18.
    Abhishree, T.M., Latha, J., Manikantan, K., Ramachandran, S.: Face recognition using Gabor filter based feature extraction with anisotropic diffusion as a pre-processing technique. Procedia Computer Sci. 45, 312–321 (2015). ISSN 1877-0509, Scholar
  19. 19.
    Sharif, M., Khalid, A., Raza, M., Mohsin, S.: Face recognition using Gabor filters. J. Appl. Comput. Sci. Math. 11 (2011)Google Scholar
  20. 20.
    Arandjelovic, R., Zisserman, A.: All about VLAD. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1578–1585 (2013).
  21. 21.
    Jegou, H., Perronnin, F., Douze, M., Sanchez, J., Perez, P., Schmid, C.: Aggregating local image descriptors into compact codes. IEEE Trans. Pattern Anal. Mach. Intell. 34, 1704 (2012)CrossRefGoogle Scholar
  22. 22.
    Vishwakarma, V., Srivastava, A.: Performance improvements in face classification using random forest. Int. J. Eng. Res. Appl. 2(3), 2384–2388 (2012)Google Scholar
  23. 23.
    Crounse, K.R., Chua, L.: Methods for image processing and pattern formation in cellular neural networks: a tutorial. Circuits Syst. I: Fundam. Theory Appl., IEEE Trans. 42, 583–601 (1995). Scholar
  24. 24.
    J´egou, H., Douze, M., Schmid, C.: Improving bag-of-features for large scale image search. IJCV 87(3), 316–336 (2010)Google Scholar
  25. 25.
    Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Lost in quantization: improving particular object retrieval in large scale image databases. In Proceedings CVPR (2008)Google Scholar
  26. 26.
    J´egou, H., Douze, M., Schmid, C.: Hamming embedding and weak geometric consistency for large scale image search. In: Proceedings ECCV (2008)Google Scholar
  27. 27.
    J´egou, H., Douze, M., Schmid, C., P´erez, P.: Aggregating local descriptors into a compact image representation. In: Proceedings CVPR (2010)Google Scholar
  28. 28.
    Mikulik, A., Perdoch, M., Chum, O., Matas, J.: Learning a fine vocabulary. In: Proceedings ECCV (2010)Google Scholar
  29. 29.
    Philbin, J., Isard, M., Sivic, J., Zisserman, A.: Descriptor learning for efficient retrieval. In: Proceedings ECCV (2010)Google Scholar
  30. 30.
  31. 31.
  32. 32.

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Ajaykumar S. Cholin
    • 1
    Email author
  • A. Vinay
    • 1
  • Aditya D. Bhat
    • 1
  • Arnav Ajay Deshpande
    • 1
  • K. N. B. Murthy
    • 1
  • S. Natarajan
    • 1
  1. 1.Centre for Pattern Recognition and Machine IntelligencePES UniversityBengaluruIndia

Personalised recommendations