An Efficient Face Detection and Recognition Method Based on Semi-Supervised Learning with Improved LPP Projection Method

Authors(3) :-Kanika Bhatia, Prof. Umesh Kumar Lilhore, Prof. Nitin Agrawal

Machine learning and pattern recognition methods play a vital role in face detection and recognition from an image. Face recognition methods have an important role in various fields e.g. security, authentication and authorization. The facial recognition method extracts facial feature from a human image face. A high accuracy is always desirable in face recognition system. Human faces have a complex multidimensional structure so required an efficient and advance method for accurate detection and recognition. It attracts researchers to work in the field of face recognition. Various existing methods e.g. pattern recognition, machine learning, Eigen faces (E.g. PCA), Fisher faces (E.g. LDA) and Laplacian Face (E.g. LAAP). Existing methods encounters with several issues e.g. detection rate, accuracy and time. In this research we are presenting an efficient and more accurate face detection and recognition method based on improved locality preserving projection and semi supervised learning method (ILPP-SSLM). A LPP method is basically an appearance based method. One extra feedback parameters are added in existing LPP which helps to improve the accuracy %. Proposed method firstly finds an embedding to preserve local information and then find a sub face to detect the desired face. Proposed method apply semi supervised learning method which uses combine features of supervised as well as unsupervised learning. Proposed method uses KNN (K-Nearest Neighbor) "supervised" classification method in that it uses the class labels of the training data and LPP dimensionality reduction method (unsupervised learning). An experimental study clearly shows that proposed method performs much better in terms of accuracy %, detection rate % over existing LPP method.

Authors and Affiliations

Kanika Bhatia
M. Tech. Research Scholar, NRI Institute of Information Science & Technology Bhopal (M.P), India
Prof. Umesh Kumar Lilhore
Head PG, NRI Institute of Information Science & Technology Bhopal (M.P), India
Prof. Nitin Agrawal
Associate Professor, NRI Institute of Information Science & Technology Bhopal (M.P), India

Face Detection, Face recognition, ILPP-SSLM, Supervised Learning, Unsupervised learning, LPP and Semi supervised learning.

  1. Jie pan, Xue-song wang, and yu-hu Cheng,” single-sample face recognition based on LPP Feature Transfer”, IEEE. Translations and content mining, year 2016, PP 1-12.
  2. M. Theresa, S.M. Poonkuzhali, K. Hemapriya and S. Raja, “Face Recognition using Unsupervised Feature Learning (UFL) Approach”, Middle-East Journal of Scientific Research 24 (S2): 238-242, 2016.
  3. Weng-Tai Su, Chih-Chung Hsu, Chia-Wen Lin, Weiyao Lin," Supervised learning based face hallucination for enhancing face recognition", ICASSP IEEE 2016, pp1751-1756.
  4. Dr. Ravish R Singh, Ronak K Khandelwal, Manoj Chavan, "face recognition using orthogonal Locality preserving projections", International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)-2016, IEEE 16, PP1323-1329.
  5. G.Suvarna Kumar P.V.G.D Prasad Reddy R.Anil Kumar Sumit Gupta, "Position Detection with Face Recognition using Image Processing and Machine Learning Techniques", IJCA Special Issue on “Novel Aspects of Digital Imaging Applications” DIA, 2011, PP 79-88.
  6. Wonjun Kim, Chanho Jung, and Simone Bianco,"Optimization for Detection and Recognition in Images and Videos ", Hindawi Mathematical Problems in Engineering Volume 2017, PP 1-3
  7. Jianzheng Liu, Chunlin Fang, and Chao Wu,"A Fusion Face Recognition Approach Based on 7-Layer Deep Learning Neural Network", Hindawi Publishing Corporation Journal of Electrical and Computer Engineering Volume 2016, PP 11-17
  8. Mehmet Korkmaz and Nihat Yilmaz,"Face Recognition by Using Back Propagation Artificial Neural Network and Windowing Method", Journal of Image and Graphics, Vol. 4, No. 1, June 2016, PP 15-20
  9. H.Arora1, K. Tayagi1, P.Sharma1, K.Jain,"A Comprehensive Comparative Performance Analysis of Eigenfaces, Laplacianfaces and Orthogonal Laplacianfaces for Face Recognition", International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering Vol. 3, Issue 5, May 2014, PP 9734-9741
  10. Tikoo S and Malik N,"Detection, Segmentation, and Recognition of Face and its Features Using Neural Network", Journal of Biosensors & Bioelectronics Volume 7 Issue 2, 2016, PP 12-19
  11. Avenir K. Troitsky,"Two-Level Multiple Face Detection Algorithm Based on Local Feature Search and Structure Recognition Methods”, International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 6 (2016) pp 4640-4647
  12. Kamini Solanki, Prashant Pittalia, Ph.D., "Review of Face Recognition Techniques", International Journal of Computer Applications (0975 8887) Volume 133 No.12, January 2016, PP 20-25.
  13. Dr. Nita Thakare1, Meghna Shrivastava2, Nidhi Kumari,"Face detection and recognition for automatic attendance system", JCSMC, Vol. 5, Issue. 4, April 2016, PP 74 -78
  14. Supriya D Kakade, "A Review Paper on Face Recognition Techniques”, International Journal for Research in Engineering Application & Management (IJREAM) ISSN: 2494-9150 Vol-02, Issue 02, MAY 2016, PP 1-4
  15. Kavita, Ms. Manjeet Kaur,"A Survey paper for Face Recognition Technologies", International Journal of Scientific and Research Publications, Volume 6, Issue 7, July 2016, PP 441-446
  16. C. Wang, H. Li, and M. Ma, “Face recognition technology based on the identification card,” in Proc. Int. Conf. Intell. Comput. Technol. Autom., 2012, pp. 173-176.
  17. M. Baktashmotlagh, M. T. Harandi, B. C. Lovell and M. Salzmann, “Unsupervised domain adaptation by domain invariant projection,” in Proc. Int. Conf. Comput. Vision, 2013, pp. 769-776.
  18. R. Ibrahim and Z. M. Zin, “Study of automated face recognition system for office door access control application,” in Proc. IEEE Int. Conf. Commun. Software Netw., 2011, pp. 132-136.
  19. J. Wang, Q. Ruan, W. Li and S. Deng, “One-shot learning gesture recognition from RGB-D data using a bag of features,” J. Mach. Learn. Res., vol. 14, pp. 2549-2582, Sep. 2013.
  20. J. Lu, Y.-P. Tan, G. Wang, “Discriminative multi mini fold analysis for face recognition from a single training sample per person,” IEEE Trans. Pattern Anal. Mach. Intell. vol. 35, no. 1, pp. 39-51, Jan. 2013

Publication Details

Published in : Volume 2 | Issue 6 | November-December 2017
Date of Publication : 2017-12-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 378-383
Manuscript Number : CSEIT1726114
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Kanika Bhatia, Prof. Umesh Kumar Lilhore, Prof. Nitin Agrawal, "An Efficient Face Detection and Recognition Method Based on Semi-Supervised Learning with Improved LPP Projection Method", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 6, pp.378-383, November-December-2017.
Journal URL :

Article Preview