Face recognition system based on principal components analysis and distance measures

  • Authors

    • T Meenpal
    • Aarti Goyal
    • Ankita Meenpal
    2018-04-20
    https://doi.org/10.14419/ijet.v7i2.21.11826
  • PCA, Eigenface, euclidian distance, mahalanobis distance, manhattan distance.

  • Abstract

    Face recognition plays a vital role and has a huge scope in the field of biometrics, image processing, artificial intelligence, pattern recognition and computer vision. This paper presents an approach to perform face recognition using Principal Components Analysis (PCA) as feature extraction technique and different distance measures as matching techniques. The proposed method is developed after the deep study of a number of face recognition methods and their outcomes. In the proposed method, Principal Components Analysis is used for facial features extraction and data representation. It generates eigenvalues of the facial images, hence, reduces the dimensionality. The recognition is produced using three different matching techniques (Euclidean, Manhattan and Mahalanobis) and the results are` presented. Yale and Aberdeen Face Databases are used to test and analyze the results of the proposed method.

     

     

  • References

    1. [1] Doermann D, Liang J & Li H, “Progress in camera-based document image analysisâ€, Proceedings. Seventh International Conference on Document Analysis and Recognition, (2003), pp.606-616.

      [2] Pandya JM, Rathod D & Jadav JJ, “A survey of face recognition approachâ€, International Journal of Engineering Research and Applications (IJERA), Vol.3, No.1,(2013), pp.632-635.

      [3] Indumathi R & Palanivel N, “Pose Invariant Face Recognition Using HMM and SVM Using PCA for Dimensionality Reductionâ€, International Conference on Information Communication and Embedded Systems (ICICES), (2014), pp.1-6.

      [4] Mahmud F, Haque ME, Zuhori ST & Pal B, “Human face recognition using PCA based Genetic Algorithmâ€, International Conference Electrical Engineering and Information & Communication Technology, (2014).

      [5] Borade SN, Deshmukh RR & Ramu S, “Face recognition using fusion of PCA and LDA: Borda count approachâ€, 24th Mediterranean Conference on Control and Automation (MED), (2016), pp.1164-1167.

      [6] Ghinea G, Kannan R & Kannaiyan S, “Gradient-Orientation-Based PCA Subspace for Novel Face Recognitionâ€, IEEE Access, Vol.2, (2014), pp.914-920.

      [7] Kasun LLC, Yang Y, Huang GB & Zhang Z, “Dimension Reduction With Extreme Learning Machineâ€, IEEE Transactions on Image Processing, Vol.25, No.8, (2016), pp.3906-3918.

      [8] Luan X, Fang B, Liu L, Yang W & Qian J, “Extracting sparse error of robust PCA for face recognition in the presence of varying illumination and occlusionâ€, Pattern Recognition, Vol.47, No.2, (2014), pp.495-508.

      [9] Turk MA & Pentland AP, “Face recognition using eigenfacesâ€, Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR), (1991), pp.586-591.

      [10] Ouarda W, Trichili H, Alimi AM & Solaiman B, “Bag of face recognition systems based on holistic approachesâ€, 15th International Conference on Intelligent Systems Design and Applications (ISDA), (2015), pp.201-206.

      [11] Jafri R & Arabnia, HR, “A survey of face recognition techniquesâ€, Jips, Vol.5, No.2, (2009), pp.41-68.

      [12] Yang M, Zhang L, Shiu SC & Zhang D, “Gabor feature based robust representation and classification for face recognition with Gabor occlusion dictionaryâ€, Pattern Recognition, Vol.46, No.7, (2013), pp.1865-1878.

      [13] Geng C & Jiang X, “Face recognition using sift featuresâ€, 16th IEEE International Conference on Image Processing, (2009), pp.3313-3316.

      [14] Perlibakas V, “Distance Measures for PCA-Based Face Recognitionâ€, Pattern Recognition Letters, Vol.25, (2004), pp.711-724.

      [15] Bashar F, Khan A, Ahmed F & Kabir H, ‘‘Face recognition using similarity pattern of image directional edge responseâ€, Adv. Elect. Comput. Eng., Vol.14, No.1, (2014), pp.69–76.

      [16] Lanitis A, Taylor CJ & Cootes TF, “Toward automatic simulation of aging effects on face imagesâ€, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.24, No.4, (2002), pp.442-455.

      [17] Fanelli G, Gall J & Van Gool L, “Real time head pose estimation with random regression forestsâ€, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2011), pp.617-624.

      [18] Fakhir MM, Woo WL, Chambers JA & Dlay SS, “Novel Method of Face Recognition from Various Poseâ€, International Conference on Pattern Recognition Systems, (2016), pp.1-6.

      [19] Karim TF, Lipu MSH, Rahman ML & Sultana F, “Face recognition using PCA-based methodâ€, IEEE International Conference on Advanced Management Science, Vol.3, (2010), pp.158-162.

      [20] Li J, Zhao B & Zhang H, “Face Recognition Based on PCA and LDA Combination Feature Extractionâ€, First International Conference on Information Science and Engineering, (2009), pp.1240-1243.

      [21] Ghimire D & Lee J, “Geometric feature-based facial expression recognition in image sequences using multi-class adaboost and support vector machinesâ€, Sensors, Vol.13, No.6, (2013), pp.7714-7734.

      [22] Thomaz CE & Giraldi GA, “A new ranking method for principal components analysis and its application to face image analysisâ€, Image and Vision Computing, Vol.28, No.6, (2010), pp.902-913.

      [23] Jabid T, Kabir MH & Chae O, “Local directional pattern (LDP) for face recognitionâ€, Digest of Technical Papers International Conference on Consumer Electronics, (2010), pp.329-330.

      [24] Zhao W, Chellappa R, Phillips PJ & Rosenfeld A, “Face recognition: A literature surveyâ€, ACM computing surveys (CSUR), Vol.35, No.4, (2003), pp.399-458.

      [25] Jain AK &Li SZ, Handbook of face recognition. New York: springer, (2011).

      [26] Ahonen T, Hadid A & Pietikainen M, “Face description with local binary patterns: Application to face recognitionâ€, IEEE transactions on pattern analysis and machine intelligence, Vol.28, No.12, (2006), pp.2037-2041.

      [27] Bouchaffra D, “Conformation-Based Hidden Markov Models: Application to Human Face Identificationâ€, IEEE Transactions on Neural Networks, Vol.21, No.4, (2010), pp.595-608.

      [28] He X, Yan S, Hu Y, Niyogi P & Zhang HJ, “Face recognition using laplacianfacesâ€, IEEE Trans. Pattern Anal. Mach. Intell., Vol.27, No.3, (2005), pp.328-340.

      [29] Anbarjafari G, ‘‘Face recognition using color local binary pattern from mutually independent color channelsâ€, EURASIP J. Image Video Process, Vol.2013, No.1, (2013), pp.1–11.

      [30] Georghiades AS, Belhumeur PN & Kriegman DJ, “From few to many: Illumination cone models for face recognition under variable lighting and poseâ€, IEEE transactions on pattern analysis and machine intelligence, Vol.23, No.6, (2001), pp.643-660.

      [31] Face Recognition Vendor Test: Overview and summary. http://www.frvt.org/

      [32] Wright J, Ma Y, Mairal J, Sapiro G, Huang TS & Yan S, “Sparse representation for computer vision and pattern recognitionâ€, Proceedings of the IEEE, Vol.98, No.6,(2010), pp.1031-1044.

      [33] The Extended Yale Face Database B,

      http://vision.ucsd.edu/leekc/ExtYaleDatabase/ExtYaleB.html.

      [34] Aberdeen face database, http://pics.stir.ac.uk/2D_face_sets.htm

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  • How to Cite

    Meenpal, T., Goyal, A., & Meenpal, A. (2018). Face recognition system based on principal components analysis and distance measures. International Journal of Engineering & Technology, 7(2.21), 15-19. https://doi.org/10.14419/ijet.v7i2.21.11826

    Received date: 2018-04-21

    Accepted date: 2018-04-21

    Published date: 2018-04-20