Conference Paper2009Cited by 34

PCA based face recognition and testing criteria

Bruce Poon, M Ashraful Amin, Hong Yan

2009 International Conference on Machine Learning and Cybernetics

IEEE, Vol. 5, pp. 2945–2949

DOI: 10.1109/icmlc.2009.5212591URL

Abstract

In this work, we use the PCA based method to build a face recognition system with a recognition rate more than 97% for the ORL and 100% for the CMU databases. However, the main goal of this research is to identify the characteristics of face recognition rates while, i) the number of training and test data is varied; ii) the amount of noise in the training and test data is varied; iii) the level of blurriness in the training and test data is varied; iv) the image size in the training and test data is varied; and v) different databases are used with aligned images. We have observed that, i) in general the increase of the number of signature on images increases the recognition rate, however, the recognition rate saturates after a certain amount of increase; ii) the increase in the number of samples used in the calculation of covariance matrix increases the recognition accuracy for a given number of individuals to identify; iii) the increase in noise and blurriness affects the recognition accuracy; iv) the reduction in image-size has very minimal effect on the recognition accuracy; v) if less number of individuals are supposed to be recognized then the recognition accuracy increases; and vi) aligned images used increases the recognition accuracy.

Keywords

  • Principal component analysis
  • face recognition
  • image blurriness
  • image size reduction
  • noise robustness
  • ORL face database
  • recognition accuracy
  • covariance matrix
  • training data size

CCDS Authors

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Professor

+1 more affiliation

Department of Information Sciences and Technology

George Mason University, USA

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

References

  1. 1.Matthew Turk, Alex Pentland. (1991). Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 3(1), 71–86[10.1162/jocn.1991.3.1.71]
  2. 2.Chengjun Liu, Harry Wechsler. (2002). Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Transactions on Image Processing, 11(4), 467–476[10.1109/tip.2002.999679]
  3. 3.Chengjun Liu. (2004). Gabor-based kernel pca with fractional power polynomial models for face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(5), 572–581[10.1109/tpami.2004.1273927]
  4. 4.Hyeonjoon Moon, P. Jonathon Phillips. (2001). Computational and Performance Aspects of PCA-Based Face-Recognition Algorithms. Perception, 30(3), 303–321[10.1068/p2896]
  5. 5.Chengjun Liu, Harry Wechsler. (2003). Independent component analysis of gabor features for face recognition. IEEE Transactions on Neural Networks, 14(4), 919–928[10.1109/tnn.2003.813829]
  6. 6.Matthew N. Dailey, Garrison W. Cottrell, Curtis Padgett, Ralph Adolphs. (2002). EMPATH: A Neural Network that Categorizes Facial Expressions. Journal of Cognitive Neuroscience, 14(8), 1158–1173[10.1162/089892902760807177]
  7. 7.Daoqiang Zhang, Zhi‐Hua Zhou, Songcan Chen. (2005). Diagonal principal component analysis for face recognition. Pattern Recognition, 39(1), 140–142[10.1016/j.patcog.2005.08.002]
  8. 8.S. Chen, Tianlong Shan, Brian C. Lovell. (2007). Robust Face Recognition in Rotated Eigen Space. Queensland's institutional digital repository (The University of Queensland)
  9. 9.Su Hongtao, Dagan Feng, Zhao Rongchun. (2003). Face recognition using multi-feature and radial basis function network. , 51–57
  10. 10.M. Ashraful Amin, Nitin Afzulpurkar, Matthew N. Dailey, Vatcharaporn Esichaikul, Dentcho N. Batanov. (2005). Fuzzy-C-Mean Determines the Principle Component Pairs to Estimate the Degree of Emotion from Facial Expressions. Lecture notes in computer science, 484–493[10.1007/11539506_62]

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  1. 1.Sasan Karamizadeh, Shahidan M. Abdullah, Azizah A. Manaf, Mazdak Zamani, Alireza Hooman. (2013). An Overview of Principal Component Analysis. Journal of Signal and Information Processing, 04(03), 173–175[10.4236/jsip.2013.43b031]
  2. 2.Jamal Hussain Shah, Muhammad Sharif, Mudassar Raza, Aisha Azeem. (2013). A Survey: Linear and Nonlinear PCA Based Face Recognition Techniques. , 10, 536–545[link]
  3. 3.Bruce Poon, M. Ashraful Amin, Hong Yan. (2011). Performance evaluation and comparison of PCA Based human face recognition methods for distorted images. International Journal of Machine Learning and Cybernetics, 2(4), 245–259[10.1007/s13042-011-0023-2]
  4. 4.Hieu Chi Phan, Huan Thanh Duong. (2020). Predicting burst pressure of defected pipeline with Principal Component Analysis and adaptive Neuro Fuzzy Inference System. International Journal of Pressure Vessels and Piping, 189, 104274[10.1016/j.ijpvp.2020.104274]
  5. 5.Erwin Hidayat, Nur A. Fajrian, Azah Kamilah Muda, Choo Yun Huoy, Sabrina Ahmad. (2011). A comparative study of feature extraction using PCA and LDA for face recognition. , 354–359[10.1109/isias.2011.6122779]
  6. 6.Muhammad Sharif, Sajjad Mohsin, Muhammad Younas Javed. (2012). A Survey: Face Recognition Techniques. Research Journal of Applied Sciences Engineering and Technology, 4(23), 4979–4990
  7. 7.Avinash Kumar Singh, G. C. Nandi. (2012). Face recognition using facial symmetry. , 550–554[10.1145/2393216.2393308]
  8. 8.T. Verma, R. K. Sahu. (2013). PCA-LDA based face recognition system & results comparison by various classification techniques. , 1–7[10.1109/icghpc.2013.6533913]
  9. 9.Sukhvinder Singh, Meenakshi Sharma, N. S. Govinda Rao. (2012). Accurate Face Recognition Using PCA and LDA. , 2(4), 101–121
  10. 10.Dean O'Reilly, Nicholas Bowring, Stuart Harmer. (2012). Signal processing techniques for concealed weapon detection by use of neural networks. , 1–4[10.1109/eeei.2012.6377063]
  11. 11.Iqbal Nouyed, Bruce Poon, M. Ashraful Amin, Hong Yan. (2015). A study on the discriminating characteristics of Gabor phase-face and an improved method for face recognition. International Journal of Machine Learning and Cybernetics, 7(6), 1115–1130[10.1007/s13042-015-0440-8]
  12. 12.K. Punnam Chandar, M. Mahesh Chandra, M. Raman Kumar, B. Swarna Latha. (2011). Multi scale feature extraction and enhancement using SVD towards secure Face Recognition system. , 3, 64–69[10.1109/icsccn.2011.6024516]
  13. 13.M. Izuddeen, M. K. Naja’atu, M. U. Ali, M. B. Abdullahi, A. M. Baballe, A. U. Tofa, M. Gambo. (2022). FPGA Based Facial Recognition System. Journal of Engineering Research and Reports, 89–96[10.9734/jerr/2022/v22i817557]
  14. 14.T. Srinivas, P. Sandeep Mohan, R. Shiva Shankar, Ch. Surender Reddy, P. V. Naganjaneyulu. (2012). Face Recognition Using PCA and Bit-Plane Slicing. Lecture notes in electrical engineering, 515–523[10.1007/978-1-4614-3363-7_60]
  15. 15.Suprachaya Veeraprasit, Suphakant Phimoltares. (2010). Neural Network-Based Teeth Recognition Using Singular Value Decomposition and Color Histogram. , 1–4[10.1109/iciecs.2010.5677780]
  16. 16.Farzaneh Atyabi, Olha Buchel, Leila Hedayatifar. (2020). Driver Countries in Global Banking Network. Entropy, 22(8), 810[10.3390/e22080810]
  17. 17.Suprachaya Veeraprasit, Suphakant Phimoltares. (2011). Hybrid feature-based teeth recognition system. , 302–305[10.1109/ist.2011.5962228]
  18. 18.Dean O'Reilly, Nicholas Bowring, Nacer Ddine Rezgui, David Andrews, Stuart Harmer. (2013). Remote concealed threat detection by novel classification algorithms applied to multi-polarimetric UWB radar. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE, 8714, 871410[10.1117/12.2016586]
  19. 19.Moh Edi Wibowo, Dian Tjondronegoro. (2011). Face Recognition across Pose on Video Using Eigen Light-Fields. , 1, 536–541[10.1109/dicta.2011.96]
  20. 20.Priyadarsini. (2011). Efficient Face Recognition in Video by Bit Planes Slicing. Journal of Computer Science, 8(1), 26–30[10.3844/jcssp.2012.26.30]
  21. 21.Veronica Scurtu, Vi Dai Ho, Marius Preda, Catherine Dang. (2011). Audiovisual Descriptors for a Linguistic Educational Engine. , 107–112[10.1109/mue.2011.30]
  22. 22.Xu Zhou, Yilin Wang, Peng Zhang, Baoxin Li. (2016). Scale-adaptive EigenEye for fast eye detection in wild web images. , 131, 2911–2915[10.1109/icip.2016.7532892]
  23. 23.Chen Chiung Hsieh, Wei Hsu Chen. (2011). A Face Recognition System Based on ASM Facial Components. Applied Mechanics and Materials, 58-60, 2314–2319[10.4028/www.scientific.net/amm.58-60.2314]
  24. 24.K. Punnam Chandar, T. Satyasavithri, M. Mahesh Kumar, M. Raman Kumar. (2011). Suitability of Complex Wavelets towards Face Recognition. , 3, 1–5[10.1109/iciip.2011.6108843]
  25. 25.Shantala Devi Patil. (2014). A Security System by using Face and Speech Detection.
  26. 26.Jiaqi Zhang, Xiaoyuan Li, Yuxia H, Zhenlong Wang. (2012). Similarity of Macaque Face Images based on PCA. [10.2991/iccia.2012.239]
  27. 27.Xu Zhou. (2018). Towards Addressing Key Visual Processing Challenges in Social Media Computing.
  28. 28.Xiao Mei Ning, Yan Shi. (2012). Research and Implementation on Face Recognition Algorithm. Applied Mechanics and Materials, 151, 657–660[10.4028/www.scientific.net/amm.151.657]
  29. 29.Henry Ndu, Akbar Sheikh-Akbari, Iosif Mporas, Jiamei Deng. (2024). Image Band-Distributive PCA Based Face Recognition Technique. Advanced sciences and technologies for security applications, 219–230[10.1007/978-3-031-47594-8_11]
  30. 30.Kehan Liu, Jun Qi, Yuanhua Du, Nan Zhou, Kaibo Shi. (2023). Sparsity Enhanced Correntropy Based Robust Principal Component Analysis. , 62, 1366–1372[10.1109/acait60137.2023.10528414]
  31. 31.Jia Qi Zhang, Xiao Yuan Li, H. Yu Xia, Zhen Long Wang. (2013). Similarity of Macaque Face Images Based on PCA. Advanced materials research, 756-759, 4116–4120[10.4028/www.scientific.net/amr.756-759.4116]
  32. 32.Pritam R. Dungarwal. (2012). Hybrid Feature-Based Teeth Recognition.
  33. 33.Claudio Cesar Silva de Freitas. (2014). Um estudo do reconhecimento de linhas palmares utilizando PCA e limiarização local adaptativa. [10.47749/t/unicamp.2014.931319]
  34. 34.M. Jasmine Pemeena Priyadarsini, I. Srinivasa Rao. (2015). RECOGNIZING THE 2D FACE USING EIGEN FACES BY PCA.
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