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Feature selection for face recognition using DCT-PCA and Bat algorithm

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Abstract

Though the face recognition systems do not impose any constraints on users and also possess several advantages. Despite that, these still present some challenges such as facial expressions, sad, pose, illumination, age changes, and noise etc. There is a need to develop such method that copes with these challenges and yields better results. This paper makes use of DCT-PCA combination to reduce the dimensionality and extract the features followed by Bat algorithm to yield a set of features that proves to be the best for face recognition under uncontrolled environment. On comparison with other meta-heuristics such as GA, PSO, and CS, the results disclose that the proposed method outperforms others even in the existence of noise also.

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Abbreviations

ABC:

Artificial bee colony

ACO:

Ant colony optimization

AI:

Artificial intelligence

CS:

Cuckoo search

DCT:

Discrete cosine transform

DE:

Differential evolution

DWT:

Discrete wavelet transform

ED:

Euclidean distance

FR:

Face recognition

GA:

Genetic algorithm

ICA:

Independent component analysis

LBP:

Local binary patterns

LDP:

Local directional pattern

MA:

Memetic algorithm

PCA:

Principle component analysis

PSO:

Particle swarm optimization

SVM:

Support vector machine

Y_I:

YaleB_Illumination

Y_P:

YaleB_Pose

References

  1. Chellappa R, Wilson CL, Sirohey S (1995) Human and machine recognition of faces: a survey. IEEE Proc 83(5):705–740

    Article  Google Scholar 

  2. ORL Cambridge face database. Retrieved 1994, from AT&T Laboratories Cambridge, http://www.cam-orl.co.uk/facedatabase.html

  3. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cognit Neurosci 3(1):71–86

    Article  Google Scholar 

  4. Maheshwari R, Kumar M, Kumar S (2016) Optimization of feature selection in face recognition system using differential evolution and genetic algorithm. In: Proceedings of fifth international conference on soft computing for problem solving, pp 363–374

  5. Agarwal V, Bhanot S (2015) Firefly inspired feature selection for face recognition. In: Eighth international conference on contemporary computing (IC3), pp 257–262

  6. Kaur H, Panchal VK, Kumar R (2013) A novel approach based on nature inspired intelligence for face feature extraction and recognition. In: Sixth international conference on contemporary computing (IC3), pp 149–153, 8–10

  7. Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: a literature survey. ACM Comput Surv 35(4):399–458

    Article  Google Scholar 

  8. Cheng G, Shi C, Zhu K, Gong K (2011) The application of binary particle swarm algorithm in face recognition. In: Seventh international conference computational intelligence and security (CIS), pp 1229–1233

  9. Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: González JR, Pelta DA, Cruz C, Terrazas G, Krasnogor N (eds) Nature inspired cooperative strategies for optimization (NICSO 2010). Studies in computational intelligence, vol 284. Springer, Berlin, Heidelberg

    Google Scholar 

  10. Ahmed N, Natarajan T, Rao KR (1974) Discrete cosine transfom. IEEE Trans Comput 23(1):90–93

    Article  MATH  Google Scholar 

  11. Kumar D, Kumar S, Rai CS (2008) Memetic algorithms for feature selection in face recognition. In: Eighth international conference on hybrid intelligent systems, pp 931–934

  12. B¨ack T (1996) Evolutionary algorithms in theory and practice—evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, Oxford

    Google Scholar 

  13. Parpinelli RS, Lopes HS (2011) New inspirations in swarm intelligence: a survey. Int J Bio-Inspir Comput 3(1):1–16

    Article  Google Scholar 

  14. Ramadan RM, Abdel-Kader RF (2009) Face recognition using particle swarm optimization-based selected features. Int J Signal Process Image Process Pattern Recognit 2(2):51–65

    Google Scholar 

  15. Yan Z, Yuan C (2004) Ant colony optimization for feature selection in face recognition. Biometric authentication, lecture notes in computer science, 3072. Springer, pp 221–226

  16. Harandi MT, Ahmadabadi MN, Araabi BN, Lucas C (2004) Feature selection using genetic algorithm and its application to face recognition. In: IEEE conference on cybernetics and intelligent systems, vol. 2, pp. 1368–1373

  17. Malhotra P, Kumar D (2017) An optimized face recognition system using cuckoo search. J Intell Syst. doi:10.1515/jisys-2017-0127

    Google Scholar 

  18. Yang XS (2013) Bat algorithm: literature review and applications. Int J Bio-Inspired Comput 5(3):141–149

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Research Scholar, GJUS&T, Hisar, Haryana, India

    Preeti

  2. Department of CSE, GJUS&T, Hisar, Haryana, India

    Dinesh Kumar

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  1. Preeti
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  2. Dinesh Kumar
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Correspondence to Preeti.

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Preeti, Kumar, D. Feature selection for face recognition using DCT-PCA and Bat algorithm. Int. j. inf. tecnol. 9, 411–423 (2017). https://doi.org/10.1007/s41870-017-0051-6

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  • DOI: https://doi.org/10.1007/s41870-017-0051-6

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