CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Face Recognition and Detection through Similarity Measurements

by Irfan Bashir, Ajay Koul
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 174 - Number 3
Year of Publication: 2017
Authors: Irfan Bashir, Ajay Koul
10.5120/ijca2017915368

Irfan Bashir, Ajay Koul . Face Recognition and Detection through Similarity Measurements. International Journal of Computer Applications. 174, 3 ( Sep 2017), 38-48. DOI=10.5120/ijca2017915368

@article{ 10.5120/ijca2017915368,
author = { Irfan Bashir, Ajay Koul },
title = { Face Recognition and Detection through Similarity Measurements },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2017 },
volume = { 174 },
number = { 3 },
month = { Sep },
year = { 2017 },
issn = { 0975-8887 },
pages = { 38-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume174/number3/28390-2017915368/ },
doi = { 10.5120/ijca2017915368 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:21:12.289087+05:30
%A Irfan Bashir
%A Ajay Koul
%T Face Recognition and Detection through Similarity Measurements
%J International Journal of Computer Applications
%@ 0975-8887
%V 174
%N 3
%P 38-48
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The facial recognition has been a problem worked on around the world for many persons, the problem has emerged in multiple fields and sciences, especially in computer science and other fields that are very interested in this technology are robotic, criminalist etc. Unfortunately, many reported face recognition techniques relay on the size and representative of training set such as e-passport, law enforcement and id- card identification, and most of them will suffer serious performance drop if only one training sample per person is available to the systems [1].In a face image, only a part of face is changed due to pose, illumination and other source of changes .In this paper, a novel face recognition detection approach known as Gabor wavelet based PCA approach is presented based on fusing global and local features of image. To extract global and local features, Gabor wavelet filter are applied on the whole image and non-overlapping sub- images with equal size. To reduce the dimension of new fused feature vector and to better characterize the similarity between each gallery face and the probe image set, Principal Component Analysis (PCA) is employed. And finally, measure the similarity between the images by using the Euclidean distance as classifier. The Experimental results shows that proposed technique improves the efficiency of face recognition under varying illumination, expression and variation in poses of face images by using standard databases when compared to traditional PCA and Conventional method such as global Gabor faces recognition. In this paper, the proposed algorithm is tested on the public and largely used ORL database.

References
  1. Xiaoyang Tana, Songcan Chen, Zhi-Hua Zhou, Fuyan Zhang, " Face Recognition from a Single Image per Person: A survey”, Chinese Academy of Sciences, Beijing 100080, Pattern Recognition 39 (2006).1725 – 1745.
  2. R. Brunelli and T. Poggio, "Face recognition: features versus templates," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.15, pp.1042- 1052, 1993.
  3. M.A.Grudin, "On internal representations in face recognition systems," Pattern Recognition, Vol.33, pp.1161-1177, 2000.
  4. B.Heisele, P. Ho, J. Wu, and T. Poggio,"Face recognition: component-based versus global approaches, “Computer Vision and Image Understanding, Vol.91, pp.6-21, 2003.
  5. A.M. Martinez, Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class, IEEE Trans. Pattern Anal. Mach. Intell. 25 (6) (2002) 748–763.
  6. A.M. Martinez, Recognizing expression variant faces from a single sample image per class, Proceedings of IEEE Computer Vision and Pattern Recognition (CVPR), 2003, pp.353–358.
  7. S. Lawrence, C.L. Giles, A. Tsoi, A. Back, Face recognition: a convolution neural-network approach, IEEE Trans. Neural Networks 8 (1)(1997) 98–113.
  8. X. Tan, S.C. Chen, Z.-H. Zhou, F. Zhang, Recognizing partially occluded, expression variant faces from single training image per person with SOM and soft kNN ensemble, IEEE Trans. Neural Networks 16 (4)(2005) 875–886.
  9. M. Zhou and H.Wei, “Face verification using Gabor wavelets and Adaboost”.
  10. L. Shen and L. Bai,"Gabor feature based face recognition using kernel methods”.
  11. P. Sankaran and K.V. Asari, “A multi-view approach on modular PCA for illumination and pose invariant face recognition”.
  12. John Woodward, Christopher Horn, Julius Gatune, and Aryn Thomas,“Biometrics :A Look at Facial Recognition”, Prepared for the Virginia State Crime Commission, 2003.
  13. RabiaJafri, and Hamid R. Arabnia,”A Survey of Face Recognition Techniques”, Journal of Information Processing Systems, Vol.5, No.2, June 2009.
  14. Cahit Gurel, “Development of a face recognition system”,Master dissertation, DEP: Mechatronics engineering, univ : Atýlým university,july 2011.
  15. B.S. Manjunath, R. Chellappa, C.V.D. Malsburg, A feature based approach to face recognition, in: Proceedings, IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, 1992, pp. 373–378.
  16. Stelvio Cimato, Marco Gamassi, Vincenzo Piuri, Daniele Sana, Roberto Sassi, and Fabio Scotti,” Personal identification and verification using multimodal biometric data”,IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety Alexandria, VA, USA, 16-17 October 2006
  17. G. I. Davida, Y. Frankel, and B. J. Matt, “On enabling secure applications through off-line biometric,” in Proceedings of the IEEE International Symposium on Security and Privacy, 1998. 1998, pp. 148–157, IEEE Press.
  18. K. J. Kantharia and G. I. Prajapati, "Facial Behavior Recognition Using So Computing Techniques: A Survey," 2015 Fifth International Conference on Advanced Computing & CommunicationTechnologies, Haryana, 2015, pp.30-34.doi: 10.1109/ACCT.2015.132.
  19. Aye Pa Pa Mya and Myint MyintSein, "Tracking of the motion path of a person from video for the overlapping case," Instrumentation and Measurement Technology Conference, 2009.I2MTC '09. IEEE, Singapore, 2009, pp. 904-908.doi: 10.1109/IMTC.2009.5168579.
  20. Sakai, T., Nagao, M., and Fujibayashi, S. ―Line extraction and pattern recognition in a photograph‖, Pattern Recognition, vol.1, pp. 233–248, 1969.
  21. Kelly, M, ―Visual identification of people by computer‖ Stanford AI Proj., Stanford, CA, Tech. Rep, 1970.
  22. W. W. W. Zou and P. C. Yuen, "Very Low Resolution Face Recognition Problem," in IEEE Transactions on Image Processing, vol. 21, no. 1, pp. 327-340, Jan. 2012. doi: 10.1109/TIP.2011.2162423.
  23. L. Fei-Fei, R. Fergus, and P. Perona, “Learning generative visual models from few training examples: an incremental Bayesian approach tested on101 object categories,” IEEE. CVPR 2004, Workshop on Generative-Model BasedVision, 2004.
  24. Sample image database of about 500 images from Andrea Mosaic. [Online] Available: http://www.andreaplanet.com/andreamosaic/.
  25. 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.
  26. J. Yang, X. Chen, and W. Kunz, "A PDA-based face recognition system," in Proceedings of sixth IEEE Workshop on Applications of Computer Vision. Orlando, Florida, 2002, pp.19-23.
  27. S. Gong, S. J. McKenna, and A. Psarrou., Dynamic Vision: From Images to Face Recognition: Imperial College Press (World Scientific Publishing Company), 2000.
  28. T. Jebara, "3D Pose Estimation andNormalization for Face Recognition," Center for Intelligent Machines, McGill University, Undergraduate Thesis May, 1996.
  29. P. J. Phillips, P. Grother, R. J. Micheals, D. M.Blackburn, E. Tabassi, and J. M. Bone, "Face Recognition Vendor Test (FRVT2002)," National Institute of Standards and Technology, Evaluation report IR6965, March, 2003.
  30. X. Q. Ding and C. Fang, "Discussions on some problems in face recognition," in Advances In Biometric Person Authentication, Proceedings, Vol. 3338, Lecture Notes In Computer Science: Springer Berlin / Heidelberg, 2004, pp.47-56.
  31. T. Kanade, "Picture Processing System by Computer Complex and Recognition of Human Faces," Kyoto University, Japan, PhD. Thesis 1973.
  32. L. Wiskott, J.-M.Fellous, N. Krüger, and C. von derMalsburg, "Face Recognition by Elastic Bunch Graph Matching," IEEETransactions on Pattern Analysis and Machine Intelligence, Vol.19, pp.775-779, 1997.
  33. L. Wiskott,R. Fellous, N. Kruger, C. von Malsburg, Face recognition by elastic bunch graph matching, IEEE Trans. Pattern Anal. Mach. Intell. 19 (7) (July 1997)775–779.
  34. B.S. Manjunath, R. Chellappa, C.V.D. Malsburg, A feature based approach to face recognition, in: Proceedings, IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, 1992, pp. 373–378.
  35. H.E. Komleh, V. Chandran, S. Sridharan, Robustness to expression variations in fractal-based face recognition, Proceedings ofISSPA-01, vol. 1, Kuala Lumpur, Malaysia, 13–16 August 2001, pp. 359–362.
  36. L. Sirovich and M. Kirby, “A Low-dimensional Procedure for the Characterization of Human Faces," Journal of the Optical Society of America: Optics, Image Science, and Vision, Vol.4, pp.519-524, 1987.
  37. R. A. Fisher, "The use of multiple measures in taxonomic problems," Annals of Eugenics, Vol.7, pp. 179-188, 1936.
  38. M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski," Face recognition by independent component analysis," IEEE Transactions onNeural Networks, Vol.13, pp.1450-1464, 2002. B. Draper, K. Baek, M. S.Bartlett
  39. P. Belhumer, J. Hespanha, D. Kriegman, “Eigenfaces vs. Fisher faces: Recognition using class specific linear projection”, Proc. Of the Fourth European Conference on Computer Vision, vol.1,April 1996,Cambridge, UK, pp.45- 58.
  40. M.S. Bartlett, J.R. Movellan, T.J. Sejnowski, “Face recognition by independent component analysis,” IEEE Trans. on Neural Networks, vol.13, no.6, November 2002, pp.1450-1464.
  41. Liu CJ, Wechsler H (2002) Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition. IEEE Trans Image Process 11(4):467–476.
  42. Liu CJ, Wechsler H (2003) Independent component analysis of Gabor feature’s for face recognition. IEEE Trans Neural Netw 14(4):919–928
  43. Liu CJ (2004) Gabor-based kernel PCA with fractional power polynomial models for face recognition. IEEE Trans PAMI 26(5):572–581.
  44. Shen L, Bai L, Fairhurst M (2006) Gabor wavelets and Generalized Discriminant Analysis for face identification and verification. Image Vis Compute (in press)
  45. J. Weng, N. Ahuja, and T. S. Huang, "Learning recognition and segmentation of 3-D objects from 3- D images," in Proceedings of the International Conference on Computer Vision (ICCV 93).Berlin, Germany, 1993, pp.121-128
  46. S. Lawrence, C. L. Giles, A. C. Tsoi, and A. D. Back, "Face Recognition: A Convolutional Neural Network Approach," IEEE Transactions on Neural Networks, Special Issue on Neural Networks and Pattern Recognition, pp.1-24, 1997
  47. A. Eleyan and H. Demirel, "Face RecognitionSystem Based on PCA and Feed forward Neural Networks," in Computational Intelligence and Bioinspired Systems, Vol.3512, Lecture Notes in Computer Science: Springer Berlin / Heidelberg, 2005, pp.935-942.
  48. G. C. Zhang, X. S. Huang, S. Z. Li, Y. S. Wang, and X. H. Wu, "Boosting local binary pattern (LBP)- based face recognition," in Advances In Biometric Person Authentication, Proceedings, Vol.3338, Lecture Notes In Computer Science, 2004, pp.179-186.
  49. Y. Freund and R. E. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting," Journal of Computer and System Sciences, Vol.55, pp.119-139,1997
  50. F. S. Samaria and A. C. Harter, "Parameterization of a stochastic model for human face identification," in Proceedings of the 2ndIEEE Workshop on Applications of Computer Vision. Sarasota, FL, USA, 1994,pp.138-142.
  51. Kruger V, Sommer G (2002) Wavelet networks for face processing. J Opt Soc Am A Opt Image Sci Vis 19(6):1112– 1119 25. Jain AK, Farrokhnia F (1991) Unsupervised texture segmentation.
  52. Zhang HH et al (2005) Gabor wavelet associative memory for face recognition. IEEE Trans Neural Netw 16(1):275–278.
  53. A. Howell and H. Buxton, "Towards unconstrained face recognition from image sequences," in Proceedings of the Second IEEE International Conference on Automatic Face and Gesture Recognition, 1996, pp.224-229.
  54. T. E. de Campos, R. S. Feris, and R. M. Cesar Jr., "A Framework for Face Recognition from Video Sequences Using GWN and Eigen feature Selection," in Workshop on Artificial Intelligence and Computer Vision. Atibaia, Brazil, 2000.
  55. S. Zhou and R. Chellappa, "Beyond a single still image: Face recognition from multiple still images and videos," in Face Processing: Advanced Modeling and Methods: Academic Press, 2005.
  56. A. Tibbalds, "Three Dimensional Human Face Acquisition for Recognition," Trinity College, University of Cambridge, Cambridge, Ph. D. Thesis March 1998.
  57. C. Hesher, A. Srivastava, and G. Erlebacher, "A novel technique for face recognition using range imaging," in Proceedings of the 7th IEEE International Symposium on Signal Processing and Its Applications, Vol.2, 2003, pp.201-204.
  58. Y. Wang, C. Chua, and Y. Ho, "Facial feature detection and face recognition from 2D and 3D images," Pattern Recognition Letters, Vol.23, pp.1191-1202, 2002.
  59. Y. Lee, H. Song, U. Yang, H. Shin, and K. Sohn, "Local feature based 3D face recognition," in Audio and Video-Based Biometric Person Authentication, Vol.3546, Lecture Notes in Computer Science: Springer Berlin / Heidelberg, 2005, pp.909-918.
  60. A. Ruifrok, A. Scheenstra, and R. C. Veltkamp, "A Survey of 3D Face Recognition Methods," in Audio and Video-based Biometric Person Authentication, Vol.3546, Lecture Notes in Computer Science: Springer Berlin / Heidelberg, 2005, pp.891-899.
  61. K. W. Bowyer, K. Chang, and P.J. Flynn, "A survey of approaches and challenges in 3D and multi-modal 3D+2Dface recognition," Computer Vision and Image Understanding, Vol.101, pp.1-15, 2006.
  62. M. Turk and A. Pentland, "Face Recognition Using Eigenfaces," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1991, pp.586-591.
  63. M. Turk and A. Pentland, "Eigenfaces for Recognition," Journal Of Cognitive Neuroscience, Vol.3, pp.71-86, 1991.
  64. D. Socolinsky, L. Wolff, J. Neuheisel, and C. Eveland,"Illumination invariant face recognition using thermal infrared imagery," in IEEE Computer Society International Conference on Computer Vision and Pattern Recognition, Vol.1. Kauai, HI, USA, 2001, pp.527-534.
  65. R. Cutler, "Face recognition using infrared images and eigenfaces," University of Maryland at College Park, College Park, MD, USA, Technical report CSC 989, 1996.
  66. S. M. H. Anvar, W. Y. Yau and E. K.Teoh, "Multiview Face Detection and Registration Requiring Minimal Manual Intervention," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 10, pp. 2484-2497, Oct. 2013. doi:10.1109/TPAMI.2013.37
  67. Bo Wu, Haizhou Ai, Chang Huang and Shihong Lao, "Fast rotation invariant multi-view face detection based on real Adaboost," Automatic Face and Gesture Recognition, 2004.Proceedings. Sixth IEEE International Conference on, 2004, pp. 79-84.doi: 10.1109/AFGR.2004.1301512
  68. M. A. Tinati, E. Namjoo and M. B. A. Haghighat, "Evaluating the effect of the eigen values on BDF classifier in face detection," Application of Information and Communication Technologies (AICT), 2011 5th International Conference on, Baku, 2011, pp. 1-5.doi: 10.1109/ICAICT.2011.6111008
  69. Chengjun Liu, "A Bayesian discriminating features method for face detection, " in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 6, pp. 725-740, June 2003.doi: 10.1109/TPAMI.2003.1201822
  70. S. Z. Li and Zhenqiu Zhang, "Float Boost learning and statistical face detection ," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 9, pp. 1112-1123, Sept. 2004.doi: 10.1109/TPAMI.2004.68
  71. Vladimir VN, "The Nature of Statistical Learning Theory. Springer", Berlin Heidelberg New York, 1995.
  72. Cascade Classification — OpenCV 2.4.13.3 documentation http://docs.opencv.org/modules/objdetect/ doc/cascade-classification.html
  73. DATABASES http://www.face-rec.org/databases
  74. L. Sirovich and M Kirby, "A low dimensional procedure for the characterization of human faces", JOSA A 4, no. 3 (1987): 519-524.
  75. R. Gottumukkal, K.V. Asari, “An improved face recognition technique based on modular PCA approach,” Pattern recog.Letters,vol.25, 2004, pp.429-436.
  76. P.N. Belhumeur and J.P. Hespanha and D.J. Kriegman,"Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection", PAMI Special Issue on Face Recognition, vol. 19, no. 7, July1997, pp. 711-720.
  77. Z.L. Zheng, F.Yang, W.A. Tan, J. Jia, J. Yang, “Gabor feature-based face recognition using supervised locality preserving projection,” Signal Processing, vol. 87, 2007, pp. 2473-2483.
  78. X. Pan, Q.Q. Ruan, “Palmprint recognition using Gabor feature based (2D) 2PCA,” Neurocomputing, vol.71 (13-15), 2008, pp. 3032- 3036.
  79. L.L. Shen, L.Bai, Michael Fairhurst, “Gabor wavelets and general discriminated analysis for face identification and verification, “Images and Vision Computing vol. 25(5), 2007, pp.553-563.
Index Terms

Computer Science
Information Sciences

Keywords

Face recognition Face detection PCA Eigenfaces Gabor Wavelet Gabor faces Dimensionality reduction.

Powered by PhDFocusTM