Abstract
This paper presents the properties of information sets that help derive local features from a face when partitioned into windows and devises the information rules from the generalized fuzzy rules for information processing that helps match the unknown test face with the known for authenticating a user. information set is constituted from the information values that result from representing the uncertainty in a type-1 fuzzy set by Hanman–Anirban entropy function. The information values are shown to be the products of information sources (gray levels) in a window and their membership function values. The Hanman filter (HF) is devised to modify the information values using a cosine function whereas the Hanman transform (HT) is devised to evaluate the information source values based on the information obtained on them. Three classifiers, namely the inner product classifier, normed error classifier, and Hanman classifier are formulated. The two feature types based on HF and HT are tested on the AT&T (ORL) database, which contains pose variations in the face images and two other face databases: Indian face Database (IIT Kanpur) and UMIST (Sheffield) using new as well as known classifiers like Euclidean distance- based, Bayesian, and support vector machine classifiers.
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References
Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353
Yager RR (1979) On the measure of fuzziness and negation part I: membership in the unit interval. Int Gen Syst 5(4):221–229
Yager RR (1980) On the measure of fuzziness and negation part II: lattices. Inf Control 44(3):236–260
Yager RR (1992) On the specificity of a possibility distribution. Fuzzy Sets Syst 50(3):279–292
Hanmandlu M (2011) Information sets and information processing. Def Sci J 61(5):405–407
Kirby M, Sirovich L (1987) Low-dimensional procedure for the characterization of human faces. Opt Soc Am 4:519–524
Kirby M, Sirovich L (1990) Application of the Karhunen–Loève procedure for the characterization of human faces. IEEE Trans Pattern Anal Mach Intell 12:831–835
Pentland A, Turk M (1991) Eigenfaces for recognition. Cognit Neurosci 3:71–86
Buhmann J, Konen M, Lades M, Lange M, Von Der Malsburg C, Vorbruggen JC, Wurtz RP (1993) Distortion invariant object recognition in the dynamic link architecture. IEEE Trans Comput 42:300–311
Von der Malsburg C, Wiskott L (1996) Recognizing faces by dynamic link matching. Neuroimage 4(3):14–18
Kawa H, Mitsumoto H, Tamura S (1996) Male/female identification from 8_6 very low resolution face images by neural network. Pattern Recognit 29:331–335
Kanade T (1973) Picture processing by computer complex and recognition of human faces. Technical report, Department of Information Science, Kyoto University
Cox IJ, Ghosn J, Yianios PN (1996) Feature-based face recognition using mixture-distance. In: Computer vision and pattern recognition. San Francisco, CA, USA, pp 209–216
Chellappa R, Manjunath BS, Von der Malsburg C (1992) A feature based approach to face recognition. In: Proceedings of the IEEE CS conference on computer vision and pattern recognition. Champaign, IL, USA. pp 373–378
Akamatsu S, Fukamachi H, Masuri N, Sakaki T, Suenaga Y (1992) An accurate and robust face identification scheme. In: Proceedings of the international conference on pattern recognition. The Hague, The Netherlands, pp 217–220
Beymer DJ (1993) Face recognition under varying Pose. Technical Report 1461, MIT Artificial Intelligence Laboratory
Malsburg CVD, Maurer T (1996) Single-view based recognition of faces rotated in Depth. In: Proceedings of the international workshop on automatic face and gesture recognition, pp 176–181
Basri R, Ullman S (1991) Recognition by linear combinations of models. IEEE Trans Pattern Anal Mach Intell 13:992–1006
Poggio T, Vetter T (1997) Linear object classes and image synthesis from a single example image. IEEE Trans Pattern Anal Mach Intell 19(7):733–742
Fellous JM, von der Malsburg C, Viskott L (1997) Face recognition by elastic bunch graph matching. IEEE Trans Pattern Anal Mach Intell 19:775–779
Belhumenur PN, Hepanha JP, Kriegman DJ (1997) Eigen faces vs fisher face: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720
He X, Hu Y, Niyogi P, Yan S, Zhang HJ (2005) Face recognition using Laplacianfaces. IEEE Trans Pattern Anal Mach Intell 17(3):328–340
Liu C, Teng X, Yu W (2006) Face recognition using discriminant locality preserving projections. Image Vis Comput 24(3):239–248
Zhu L, Zhu S (2007) Face recognition based on orthogonal discriminant locality preserving projections. Neurocomputing 70(7–9):1543–1546
Wang X, Yu X (2008) Uncorrelated discriminant locality preserving projections. IEEE Signal Process Lett 15(5):361–364
Dai DQ, Yuen PC (2007) Face recognition by regularized discriminant analysis. IEEE Trans Syst Man Cybern B Cybern 37(4):1080–1085
Lu J, Tan Y-P (2010) Regularized locality preserving projections and its extensions for face recognition. IEEE Trans Syst Man Cybern B Cybern 40(3):958–963
Yang J, Yu H (2001) A direct LDA algorithm for high-dimensional data with application to face recognition. Pattern Recognit 34(10):2067–2070
Jiang T, Li H, Zhang K (2006) Efficient and robust feature extraction by maximum margin criterion. IEEE Trans Neural Netw 17(1):157–165
Yang J, Yang JY (2002) From image vector to matrix: a straightforward image projection technique–IMPCA vs. PCA. Pattern Recognit 35(9):1997–1999
Frangi F, Yang J, Yang JY, Zhang D (2004) Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans Pattern Anal Mach Intell 26(1):131–137
Li M, Yuan B (2005) 2D-LDA: a statistical linear discriminant analysis for image matrix. Pattern Recognit Lett 26(5):527–532
Yang J, Yang JY, Yong X, Zhang DX (2005) Two-dimensional discriminant transform for face recognition. Pattern Recognit 38(7):1125–1129
Zhang D, Zhou ZH (2005) (2D) 2PCA: two-directional two-dimensional PCA for efficient face representation and recognition. Neurocomputing 69(1–3):224–231
Hemantha Kumar G, Noushath S, Shivakumara P (2006) (2D)2LDA: an efficient approach for face recognition. Pattern Recognit 39(7):1396–1400
Ye J (2005) Generalized low rank approximations of matrices. Mach Learn 61(1–3):167–191
Janardan R, Li Q, Ye J (2005) Two-dimensional linear discriminant analysis. In: Advances in neural information processing systems, vol 17. MIT Press, Cambridge, pp 1569–1576
Wang K, Yang J, Zhang D, Zuo WM (2006) BDPCA plus LDA: a novel fast feature extraction technique for face recognition. IEEE Trans Syst Man Cybern B Cybern 36(4):946–953
Du Shan, RababKreidieh Ward (2009) Improved face representation by nonuniform multilevel selection of Gabor convolution features. IEEE Trans Syst Man Cybern B Cybern 39(6):1408–1419
Hu Q, Shiu S, Zhang L, Zhu P (2012) Multi-scale Patch based Collaborative Representation for Face Recognition with Margin Distribution Optimization. In: ECCV
Das A, Hanmandlu M (2011) Content-based image retrieval by information theoretic measure. Def Sci J 61(5):415–430
Hanmandlu M, Mamata (2013) Robust ear based authentication using local principal independent components. Expert Syst Appl 40(16):6478–6490
Hanmandlu M, Mamata (2014) Robust authentication using the unconstrained infra-red face images. Expert Syst Appl 41(14):6494–6511
Kreinovich V, Pedrycz W, Skowron A (2008) Handbook of granular computing. Wiley, West Sussex
Bargiela AA, Pedrycz W (2009) Human-centric information processing through granular modelling. Springer, Berlin
Hanmandlu M, Jha D (2006) An optimal fuzzy system for color image enhancement. IEEE Trans Image Process 15(10):2956–2966
Ahmad N, Azeem MF, Hanmandlu M (2003) Structure identification of generalized adaptive neuro-fuzzy inference systems. IEEE Trans Fuzzy Syst 11(5):666–681
Hanmandlu M, Verma NK (2007) From a gaussian mixture model to non-additive fuzzy systems. IEEE Trans Fuzzy Syst 15(5):809–827
Sayeed F, Hanmandlu M (2016) Three information set based feature types for the recognition of faces. Signal Image Video Process 10(2):327–334
Grover J, Hanmandlu M (2015) Hybrid fusion of score level and adaptive fuzzy decision level fusions for the finger knuckle print based authentication. Appl Soft Comput 31:1–13
Agarwal M, Hanmandlu M (2016) Representing uncertainty with information sets. IEEE Trans Fuzzy Syst 24(1):1–15
Hanmandlu M, Mamta (2014) A new entropy function and a classifier for thermal face recognition. Eng Appl Artif Intell 36:269–286
Hanmandlu M, Mamta (2015) Multimodal biometric system built on the new entropy function for feature extraction and the refined scores as a classifier. Expert Syst Appl 42:3702–3723
Pep E, Klement EP, Mesiar R (2000) Triangular norms. Kluwer Academic Publications, The Netherlands
Samaria F, Harter A (1994) Parameterization of a stochastic model for human face identification. In: Proceedings of 2nd IEEE workshop on applications of computer vision. Sarasota, FL
Mukherjee A, Vidit J (2002) The Indian FaceDatabase. http://viswww.cs.umass.edu/~vidit/IndianFaceDatabase/,2002
Bruce V, Fogelman-Soulie F, Huang TS, Phillips PJ, Wechsler H (1998) Face recognition: from theory to applications, NATO ASI series F. Comput Syst Sci 163:446–456
Libor Spacek’s facial Image database. http://cswww.essex.ac.uk/mv/allfaces/faces95.html
Giraldi GA, Thomaz CE (2010) A new ranking method for principal components analysis and its application to face image analysis. Image Vis Comput 28(6):902–913
Duda RO, Hart PE, Stork DG (2001) Pattern classification. Wiley, New York
Duin RPW, Juszczak P, Paclik P, Pekalska E, Ridder D, de Tax DMJ, Verzakov S (2007) PRTools4.1, A Matlab toolbox for pattern recognition. Delft University of Technology
Chang C, Lin C (2011) LIBSVM: a library for support vector machines 27. ACM Trans Intell Syst Technol 2(27):1–27
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Sayeed, F., Hanmandlu, M. Properties of information sets and information processing with an application to face recognition. Knowl Inf Syst 52, 485–507 (2017). https://doi.org/10.1007/s10115-016-1017-x
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DOI: https://doi.org/10.1007/s10115-016-1017-x