Abstract
Face biometric has received more attention to recognize a person in a right way. However, the face recognition is considered to be hard due to size, ethnicity, illumination, pose, various expression, and age. In this work, a novel approach is proposed to recognize the human face based on K-nearest neighbor (KNN) with particle swarm optimization (PSO). Initially, the features are extracted using local binary pattern. The metaheuristic optimization algorithms such as genetic algorithm, PSO, and ant colony optimization are investigated for feature selection. The KNN classifier is optimized using the population-based metaheuristic algorithm PSO. Finally, the face recognition is performed using the proposed PSO–KNN algorithm. In this research, experiments have been conducted on real-time face images collected from 155 subjects each with ten orientations using Logitech Webcam and also on ORL face dataset. The experimental result of the proposed PSO–KNN is compared with other benchmark recognition techniques such as decision table, support vector machine, multilayer perceptron and conventional KNN, to conclude the efficacy of the proposed approach.
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References
Jain AK, Ross AA, Nandakumar K (2011) Introduction to Biometrics. Springer, Berlin, pp 1–49
Kandaswamy C, Monteiro JC, Silva LM, Cardoso JS (2017) Multi-source deep transfer learning for cross-sensor biometrics. Neural Comput Appl 28(9):2461–2475
Jain AK, Ross AA, Prabhakar Salil (2004) An introduction to biometric recognition. IEEE Trans Circuits Syst Video Technol 14(1):4–20
Wang J, Xiong R, Chu J (2015) Facial feature points detecting based on Gaussian Mixture Models. Pattern Recogn Lett 53:62–68
Cui D, Zhang G, Hu K, Han W, Huang GB (2018) Face recognition benchmark with ID photos. Artificial intelligence and robotics. Springer, Berlin, pp 27–35
Wegrzyn M, Vogt M, Kireclioglu B, Schneider J, Kissler J (2017) Mapping the emotional face. How individual face parts contribute to successful emotion recognition. PLoS ONE 12(5):e0177239
Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041
Thangavel K, Manavalan R (2014) Soft computing models based feature selection for TRUS prostate cancer image classification. J Soft Comput 18(6):1165–1176
Liu Z-X, Huang Z-Q, Zhang H (2013) A novel face recognition approach based on two-step test sample representation. Math Probl Eng 2013:895051
Gao Y, Lee HJ (2017) Pose-invariant features and personalized correspondence learning for face recognition. Neural Comput Appl. https://doi.org/10.1007/s00521-017-3035-3
Agarwal V, Bhanot S (2017) Radial basis function neural network-based face recognition using firefly algorithm. Neural Comput Appl. https://doi.org/10.1007/s00521-017-2874-2
Zhu Y, Xue J (2017) Face recognition based on random subspace method and tensor subspace analysis. Neural Comput Appl 28(2):233–244
Gunther M, El Shafey L, Marcel S (2016) Face recognition in challenging environments: an experimental and reproducible research survey. Face recognition across the imaging spectrum. Springer, Berlin, pp 247–280
Jadoon W, Zhang L, Zhang Y (2015) Extended collaborative neighbor representation for robust single-sample face recognition. Neural Comput Appl 26(8):1991–2000
Lu Y, Cui J, Fang X (2014) Enhancing sparsity via full rank decomposition for robust face recognition. Neural Comput Appl 25(5):1043–1052
Kamencay P, Zachariasova M, Hudec R, Jarina R, Benco M, Hlubik J (2013) A novel approach to face recognition using image segmentation based on spca-knn method. Radioengineering 22(1):92–99
Xu Y, Zhu Q (2013) A simple and fast representation-based face recognition method. Neural Comput Appl 22(7–8):1543–1549
Jabid T, Kabir MH, Chae O (2010) Local directional pattern (LDP) for face recognition. In: IEEE conference on in consumer electronics (ICCE), 2010 digest of technical papers, pp 329–330
Nazir M, Ishtiaq M, Batool A, Jaffar A, Mirza M (2010) Feature selection for efficient gender classification. In: Proceedings of the 11th WSEAS international conference, pp 70–75
Sun N, Zheng W, Sun C, Zou C, Zhao L (2006) Gender classification based on boosting local binary pattern. In: Proceedings of the 3rd international conference on advances in neural networks, Chengdu, China, pp 194–201
Zhang B, Gao Y, Zhao S, Liu J (2010) Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE Trans Image Process 19(2):533–544
Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987
Ghosh S, Bandyopadhyay SK (2015) Gender classification and age detection based on human facial features using multi-class SVM. Br J Appl Sci Technol 10(40):1–15
Nanni L, Lumini A (2008) Local binary patterns for a hybrid fingerprint matcher. Pattern Recogn 41(11):3461–3466
Thangavel K, Pethalakshmi A (2009) Dimensionality reduction based on rough set theory: a review. Appl Soft Comput 9(1):1–12
Kuncheva L (1993) Genetic algorithm for feature selection for parallel classifiers. Inf Process Lett 46(4):163–168
Altun AA (1993) A combination of genetic algorithm, particle swarm optimization and neural network for palmprint recognition. J Neural Comput Appl 22(1):27–33
Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: Proceedings of IEEE international conference on CEC, pp 69–73
Dorigo M, Maniezzo V, Colorni A (1996) The ant system: optimization by a colony of cooperative agents. IEEE Trans Syst Man Cybern Part B 26(1):1–13
Kashef S, Nezamabadi H (2015) An advanced ACO algorithm for feature subset selection. Neurocomputing 147:271–279
Kumar M, Jindal MK, Sharma RK (2011) k-nearest neighbor based offline handwritten Gurmukhi character recognition. In: IEEE conference on image information processing (ICIIP), pp 1–4
The ORL Database of Faces. http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html
Vinay A, Shekhar VS, Manjunath N, Murthy KB, Natarajan S (2018) Expediting automated face recognition using the novel ORB2-IPR framework. In: Proceedings of international conference on cognition and recognition. Springer, p 223
López-Sánchez D, Corchado JM, Arrieta AG (2017) A CBR system for efficient face recognition under partial occlusion. In: International conference on case-based reasoning. Springer, pp 170–184
Chen Z, Huang W, Lv Z (2017) Towards a face recognition method based on uncorrelated discriminant sparse preserving projection. Multimed Tools Appl 76(17):17669–17683
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Authors would like to thank UGC, New Delhi, for the financial support received under UGC Major Research Project No. 43-274/2014(SR).
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Sasirekha, K., Thangavel, K. Optimization of K-nearest neighbor using particle swarm optimization for face recognition. Neural Comput & Applic 31, 7935–7944 (2019). https://doi.org/10.1007/s00521-018-3624-9
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DOI: https://doi.org/10.1007/s00521-018-3624-9