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Improved immune computation for high-precision face recognition

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Abstract

Dangerous persons can be monitored accurately and quickly using the high-precision recognition of the relative facial images via the image sensors. To increase the recognition rate of face recognition by improving the immune algorithm, the immune computation was redesigned for better face recognition to decrease the facial disturbances of the pose, illumination and expression (PIE), in this paper. The clonal selection algorithm was improved with the modification of the affinity and the algorithm workflow. The improved clonal selection algorithm searches the most similar antibody sample against the antigen of an unknown facial image, according to the affinity between the antigen and the antibody. The unknown facial images were recognized with this improved affinity and the uncertainty-based reasoning, so the affinity matching of the antibody with the unknown antigen was also improved. Experimental results show that this immune algorithm outperforms some state-of-the-art algorithms in the face recognition accuracy tests with such facial image databases as AR, Yale and CMU-PIE. So the proposed immune algorithm is useful and effective to improve the performance of face recognition in the image sensor network.

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References

  • Azarov IS, Vashkevich MI, Petrovskii AA (2014) Real-time voice conversion using artificial neural network with rectified linear units. Neurocomput Dev Appl 5:18–28

    Google Scholar 

  • Bhatt HS, Bharadwaj Singh SR, Vatsa M (2013) Recognizing surgically altered face images using multiobjective evolutionary algorithm. IEEE Trans Inf Forensics Secur 8(1):89–100

    Article  Google Scholar 

  • Cai D, He X, Han J, Zhang HJ (2006) Orthogonal Laplacianfaces for face recognition. IEEE Trans Image Process 15(11):3608–3614

    Article  Google Scholar 

  • Campelo F, Guimarães FG, Igarashi H, Ramírez JA (2005) A clonal selection algorithm for optimization in electromagnetics. IEEE Trans Magn 41(5):1736–1739

    Article  Google Scholar 

  • Castro LND, Zuben FJV (2002) Learning and optimization using the clonal selection principle. IEEE Trans Evol Comput 6(3):239–251

    Article  Google Scholar 

  • Center for Computational Vision and Control (2011) Yale face database. http://cvc.yale.edu/projects/yalefaces/yalefaces.html

  • Chan TH, Jia K, Gao SH, Lu JW (2015) PCANet: a simple deep learning baseline for image classification? IEEE Trans Image Process 24(12):5017–5032

    Article  MathSciNet  Google Scholar 

  • Chen YN, Han CC, Wang CT, Fan KC (2011) Face recognition using nearest feature space embedding. IEEE Trans Pattern Anal Mach Intell 33(6):1073–1086

    Article  Google Scholar 

  • Chen XM, Liu WQ, Lai JH, Li Z, Lu C (2012) Face recognition via local preserving average neighborhood margin maximization and extreme learning machine. Soft Comput 16(9):1515–1523

    Article  Google Scholar 

  • Chen YC, Patel VM, Phillips PJ, Chellappa R (2015) Dictionary-based face and person recognition from unconstrained video. IEEE Access 3:1783–1798

    Article  Google Scholar 

  • Cornelius CJ, Nguyen MA, Hayes CC, Makena R (2013) Supporting virtual collaboration in spatial design tasks: are surrogate or natural gestures more effective? IEEE Trans Hum Mach Syst 43(1):92–101

    Article  Google Scholar 

  • Dayhoff JE, DeLeo JM (2001) Artificial neural networks. Cancer 91(S8):1615–1635

    Article  Google Scholar 

  • Déniz O, Bueno G, Salido J, Torre FDL (2011) Face recognition using histograms of oriented gradients. Pattern Recognit Lett 32:1598–1603

    Article  Google Scholar 

  • Di Caro G, Ducatelle F, Gambardella LM (2005) Anthocnet: an adaptive nature inspired algorithm for routing in mobile ad hoc networks. Eur Trans Telecommun 16(2):443–455

    Article  Google Scholar 

  • Fernandez-Leon JA, Acosta GG, Rozenfeld A (2014) How simple autonomous decisions evolve into robust behaviours?: A review from neurorobotics, cognitive, self-organized and artificial immune systems fields. BioSystems 124:7–20

    Article  Google Scholar 

  • Galbally J, Marcel S, Fierrez J (2014) Biometric antispoofing methods: a survey in face recognition. IEEE Access 2:1530–1552

    Article  Google Scholar 

  • Gao L, Hailu A (2010) Comprehensive learning particle swarm optimizer for constrained mixed-variable optimization problems. Int J Comput Intell Syst 3(6):832–842

    Article  Google Scholar 

  • Gao L, Ding YS, Ren LH (2004) A novel ecological network-based computation platform as grid middleware system. Int J Intell Syst 19(10):859–884

    Article  Google Scholar 

  • Gao L, Ding YS, Ying H (2006) An adaptive social network-inspired approach to resource discovery for the complex grid systems. Int J Gen Syst 35(3):347–360

    Article  MATH  Google Scholar 

  • Gao SC, Tang Z, Dai HW, Zhang JC (2007) An improved clonal selection algorithm and its application to traveling salesman problems. IEICE Trans Fundam E90–A(12):2930–2938

    Article  Google Scholar 

  • Gong T (2012) High-precision immune computation for secure face recognition. Int J Secur Appl 6(2):293–298

    Google Scholar 

  • He X, Yan S, Hu Y, Niyogi P, Zhang HJ (2005) Face recognition using Laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340

    Article  Google Scholar 

  • Jiang XD, Mandal B, Kot A (2006) Enhanced maximum likelihood face recognition. Electron Lett 42(19):1089–1090

    Article  Google Scholar 

  • Lima FPA, Lotufo ADP, Minussi CR (2015) Wavelet-artificial immune system algorithm applied to voltage disturbance diagnosis in electrical distribution systems. IET Gener Transm Distrib 9(11):1104–1111

    Article  Google Scholar 

  • Luh GC (2011) Face recognition using PCA based immune networks with single training sample per person. In: ICMLC’11 proceedings of 2011 international conference on machine learning and cybernetics, vol 4, Guilin, pp 1773–1779

  • Ma WP, Shang RH, Jiao LC (2006) A novel clonal selection algorithm for face detection. In: Proceedings of the 19th Australian joint conference on artificial intelligence: advances in artificial intelligence, Hobart, pp 799–807

  • Marques I, Grana M (2012) Face recognition with lattice independent component analysis and extreme learning machines. Soft Comput 16(9):1525–1537

    Article  Google Scholar 

  • Martinez A, Benavente R (1998) The AR face database. Tech. Rep. 24, CVC

  • Muhamad AS, Deris S (2013) An artificial immune system for solving production scheduling problems: a review. Artif Intell Rev 39:97–108

    Article  Google Scholar 

  • Paul PP, Gavrilova ML, Alhajj R (2014) Decision fusion for multimodal biometrics using social network analysis. IEEE Trans Syst Man Cybern Syst 44(11):1522–1533

    Article  Google Scholar 

  • Razzak MI, Khan MK, Alghathbar K (2011) Energy efficient distributed face recognition in wireless sensor network. Wirel Pers Commun 60(3):571–582

    Article  Google Scholar 

  • Razzak MI, Elmogy BA, Khan MK et al (2012) Efficient distributed face recognition in wireless sensor network. Int J Innov Comput Inf Control 8(4):2811–2822

    Google Scholar 

  • Silva JCD, Lima FPDA, Lotufo ADP, Batista JMMDCP (2015) Identity recognition using an artificial intelligence based on artificial immune system. In: APCASE’15 proceedings of 2015 Asia-Pacific conference on computer aided system engineering, Quito, pp 158–162

  • Sunny MVN, Dwivedi R et al (2013) Gases/odors identification with artificial immune recognition system using thick film gas sensor array responses. IEEE Sens J 13(8):3039–3045

    Article  Google Scholar 

  • Thumati BT, Halligan GR, Jagannathan S (2013) A novel fault diagnostics and prediction scheme using a nonlinear observer with artificial immune system as an online approximator. IEEE Trans Control Syst Technol 21(3):569–578

    Article  Google Scholar 

  • Timmis J, Hone A, Stibor T, Clark E (2008) Theoretical advances in artificial immune systems. Theoret Comput Sci 403(1):11–32

    Article  MathSciNet  MATH  Google Scholar 

  • Wang SW, Zhuo ZZ, Yang HY, Li HY (2014) An approach to facial expression recognition integrating radial basis function kernel and multidimensional scaling analysis. Soft Comput 18(7):1363–1371

    Article  Google Scholar 

  • Yang J, Chu DL, Zhang L, Xu Y et al (2013) Sparse representation classifier steered discriminative projection with applications to face recognition. IEEE Trans Neural Netw Learn Syst 24(7):1023–1035

    Article  Google Scholar 

Download references

Funding

This study was funded by the Grants from National Natural Science Foundation of China (61673007, 61271114), Shanghai Natural Science Foundation (13ZR1400200) and the Fundamental Research Funds for the Central Universities at Donghua Univ. (2232013A3-09).

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Authors and Affiliations

  1. College of Information Science and Technology, Donghua University, Shanghai, 201620, China

    Tao Gong

  2. Engineering Research Center of Digitized Textile and Fashion Technology, Ministry of Education, Donghua University, Shanghai, 201620, China

    Tao Gong

Authors
  1. Tao Gong
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Corresponding author

Correspondence to Tao Gong.

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Conflict of interest

Tao Gong declares that he has no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by Y. Jin.

Appendix

Appendix

See Figs. 8, 9, 10.

Fig. 9
figure 9

Partial samples from the Yale facial image database

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Fig. 10
figure 10

Some samples of the CMU-PIE facial image database

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Gong, T. Improved immune computation for high-precision face recognition. Soft Comput 21, 5989–5999 (2017). https://doi.org/10.1007/s00500-016-2463-9

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