A Structural Support Vector Machine Approach for Biometric Recognition
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32926
A Structural Support Vector Machine Approach for Biometric Recognition

Authors: Vishal Awasthi, Atul Kumar Agnihotri


Face is a non-intrusive strong biometrics for identification of original and dummy facial by different artificial means. Face recognition is extremely important in the contexts of computer vision, psychology, surveillance, pattern recognition, neural network, content based video processing. The availability of a widespread face database is crucial to test the performance of these face recognition algorithms. The openly available face databases include face images with a wide range of poses, illumination, gestures and face occlusions but there is no dummy face database accessible in public domain. This paper presents a face detection algorithm based on the image segmentation in terms of distance from a fixed point and template matching methods. This proposed work is having the most appropriate number of nodal points resulting in most appropriate outcomes in terms of face recognition and detection. The time taken to identify and extract distinctive facial features is improved in the range of 90 to 110 sec. with the increment of efficiency by 3%.

Keywords: Face recognition, Principal Component Analysis, PCA, Linear Discriminant Analysis, LDA, Improved Support Vector Machine, iSVM, elastic bunch mapping technique.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 448


[1] A. K. Jain, R. Bolle, and S. Pankanti, ”Biometrics: Personal Identification in Networked Security,” A. K. Jain, R. Bolle, and S. Pankanti, Eds.: Kluwer Academic Publishers, 1999.
[2] K. Kim, ”Intelligent Immigration Control System by Using Passport Recognition and Face Verification,” in International Symposium on Neural Networks. Changing, China, 2005, pp.147-156.
[3] J. N. K. Liu, M. Wang, and B. Feng, ”iBotGuard: an Internet-based intelligent robot security system using invariant face recognition against intruder,” IEEE Transactions on Systems Man And Cybernetics Part C-Applications And Reviews, Vol.35, pp.97-105, 2005.
[4] Sharif,M., Naz, F., Yasmin, M., Shahid, M.A., Rehman, A.: Face recognition: a survey. J. Eng. Sci. Technol. Rev. 10(2), 166–177 (2017)
[5] Ahonen, T., Hadid, A., Pietika, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)
[6] Lei, Z., Wang, C., Wang, Q., Huang, Y.: Real-time face detection and recognition for video surveillance applications. In: 2009 World Congress on Computer Science and Information Engineering Real-time, pp. 168–172 (2009)
[7] P. J. Phillips, H. Moon, P. J. Rauss, and S. A. Rizvi, ”The FERET Evaluation Methodology for Face Recognition Algorithms,” IEEE Transactions onPattern Analysis and Machine Intelligence, Vol.22, pp.1090-1104, 2000.
[8] Chawla, D., Trivedi, M.C.: A comparative study on face detection techniques for security surveillance. In: Advances in Computer and Computational Sciences, pp. 531–541 (2018)
[9] Luo, Z., Hu, J., Deng,W., Shen, H.: Deep unsupervised domain adaptation for face recognition.In: 13th IEEE International Conference on Automatic Face & Gesture Recognition, pp. 453–457 (2018)
[10] E. Acosta, L. Torres, A. Albiol, and E. J. Delp, ”An automatic face detection and recognition system for video indexing applications,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Vol.4. Orlando, Florida, 2002, pp.3644-3647.
[11] J.H. Lee and W.Y. Kim, ”Video Summarization and Retrieval System Using Face Recognition and MPEG-7 Descriptors,” in Image and Video Retrieval, Vol.3115, Lecture Notes in Computer Science: Springer Berlin / Heidelberg, 2004, pp.179-188.
[12] Q. Zhang, M. Zhang, T. Chen, Z. Sun, Y. Ma, and B. Yu, “Recent advances in convolutional neural network acceleration,” Neurocomputing, vol. 323, no. 61176031, pp. 37–51, 2019.