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Face sketch recognition using a hybrid optimization model

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Abstract

In this work, a hybrid optimization-based model is introduced to handle the problem of face sketch recognition. The proposed model comprises a total of three layers that are global search layer, control layer, and fine-tuning layer. The global layer contains a set of search operations from particle swarm optimization (PSO) algorithm to perform the task of global search. However, the control layer is responsible about controlling the execution of the implemented search operations at run time. Finally, the fine-tuning layer is aimed at performing search refinement to enhance the search ability. For sketch recognition, the proposed hybrid model is applied on the input face sketch to locate the internal sketch facial components. Three types of texture features extraction techniques are adopted in this study including Histogram Of Gradient (HOG), Local Binary Pattern (LBP), and Gabor wavelet. To assess the performances of the proposed model, a total of three face sketch databases have been used which are LFW, AR, and CUHK. The reported results indicate that the proposed hybrid model was able to achieve a competitive performance with 96% on AR, 87.68% on CUHK, and 50.00% on LFW. Additionally, the outcomes reveal that the proposed model statistically outperforms others PSO-based models as well as the state-of-the-art meta-heuristic optimization models.

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References

  1. Peng C, Gao X, Wang N, Li J (2017) Superpixel-based face sketch–photo synthesis. IEEE Trans Circuits Syst Video Technol 27(2):288–299

    Article  Google Scholar 

  2. Gao X, Zhong J, Tao D, Li X (2008) Local face sketch synthesis learning. Neurocomputing 71(10–12):1921–1930

    Article  Google Scholar 

  3. Zhang S, Gao X, Wang N, Li J (2017) Face sketch synthesis from a single photo–sketch pair. IEEE Trans Circuits Syst Video Technol 27(2):275–287

    Article  Google Scholar 

  4. Tu C-T, Chan Y-H, Chen Y-C (2016) Facial sketch synthesis using 2D direct combined model-based face-specific markov network. IEEE Trans Image Process 25:3546–3561

    Article  MathSciNet  MATH  Google Scholar 

  5. Wang N, Li J, Tao D, Li X, Gao X (2013) Heterogeneous image transformation. Pattern Recognit Lett 34:77–84

    Article  Google Scholar 

  6. Bhatt HS, Bharadwaj S, Singh R, Vatsa M (2012) Memetically optimized MCWLD for matching sketches with digital face images. IEEE Trans Inf Forensics Secur 7:1522–1535

    Article  Google Scholar 

  7. Hu H, Klare BF, Bonnen K, Jain AK (2013) Matching composite sketches to face photos: a component-based approach. IEEE Trans Inf Forensics Secur 8:191–204

    Article  Google Scholar 

  8. Klare BF, Zhifeng L, Jain AK (2011) Matching forensic sketches to mug shot photos. IEEE Trans Pattern Anal Mach Intell 33:639–646

    Article  Google Scholar 

  9. Xiaoou T, Xiaogang W (2004) Face sketch recognition. IEEE Trans Circuits Syst Video Technol 14:50–57

    Article  Google Scholar 

  10. Bhatt HS, Bharadwaj S, Singh R, Vatsa M (2010) On matching sketches with digital face images, biometrics: theory applications and systems (BTAS). In: 2010 Fourth international conference on IEEE, pp 1–7

  11. Samma H, Lim CP, Saleh JM (2016) A new reinforcement learning-based memetic particle swarm optimizer. Appl Soft Comput 43:276–297

    Article  Google Scholar 

  12. Wu Z, Wu Z, Zhang J (2016) An improved FCM algorithm with adaptive weights based on SA-PSO. Neural Comput Appl 28:1–6

    Article  Google Scholar 

  13. Samma H, Lim CP, Saleh JM, Suandi SA (2016) A memetic-based fuzzy support vector machine model and its application to license plate recognition. Memetic Comput 8:1–17

    Article  Google Scholar 

  14. Abd-Elazim S, Ali E (2013) A hybrid particle swarm optimization and bacterial foraging for optimal power system stabilizers design. Int J Electr Power Energy Syst 46:334–341

    Article  Google Scholar 

  15. Abd-Elazim S, Ali E (2013) Synergy of particle swarm optimization and bacterial foraging for TCSC damping controller design. Int J WSEAS Trans Power Syst 8:74–84

    Google Scholar 

  16. Abd-Elazim S, Ali E (2013) Power system stability enhancement via bacteria foraging optimization algorithm, Arabian Journal for Science & Engineering, vol 38. Springer, Berlin

    Google Scholar 

  17. Kamboj VK (2016) A novel hybrid PSO–GWO approach for unit commitment problem. Neural Comput Appl 27:1643–1655

    Article  Google Scholar 

  18. Subasi A, Kevric J, Canbaz MA (2017) Epileptic seizure detection using hybrid machine learning methods. Neural Comput Appl 1–9. https://doi.org/10.1007/s00521-017-3003-y

  19. Sun S, Li J (2014) A two-swarm cooperative particle swarms optimization. Swarm Evolut Comput 15:1–18

    Article  Google Scholar 

  20. Lim WH, Mat Isa NA (2013) Two-layer particle swarm optimization with intelligent division of labor. Eng Appl Artif Intel 26:2327–2348

    Article  Google Scholar 

  21. Lim WH, Mat Isa NA (2014) Teaching and peer-learning particle swarm optimization. Appl Soft Comput 18:39–58

    Article  Google Scholar 

  22. Lim WH, Mat Isa NA (2014) Particle swarm optimization with increasing topology connectivity. Eng Appl Artif Intel 27:80–102

    Article  Google Scholar 

  23. Li X (2010) Niching without niching parameters: particle swarm optimization using a ring topology. IEEE Trans Evolut Comput 14:150–169

    Article  Google Scholar 

  24. Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evolut Comput 8:204–210

    Article  Google Scholar 

  25. de Oca MAM, Stutzle T, Birattari M, Dorigo M (2009) Frankenstein’s PSO: a composite particle swarm optimization algorithm. IEEE Trans Evolut Comput 13:1120–1132

    Article  Google Scholar 

  26. Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evolut Comput 10:281–295

    Article  Google Scholar 

  27. Zhi-Hui Z, Jun Z, Yun L, Chung HSH (2009) Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern Part B Cybern 39:1362–1381

    Article  Google Scholar 

  28. Hu M, Wu T, Weir JD (2013) An adaptive particle swarm optimization with multiple adaptive methods. IEEE Trans Evolut Comput 17:705–720

    Article  Google Scholar 

  29. Zhang W, Ma D, Wei J-J, Liang H-F (2014) A parameter selection strategy for particle swarm optimization based on particle positions. Expert Syst Appl 41:3576–3584

    Article  Google Scholar 

  30. Çomak E (2016) A modified particle swarm optimization algorithm using Renyi entropy-based clustering. Neural Comput Appl 5:1381–1390

    Article  Google Scholar 

  31. Wang X, Tang X (2009) Face photo-sketch synthesis and recognition. IEEE Trans Pattern Anal Mach Intell 31:1955–1967

    Article  Google Scholar 

  32. Zhou H, Kuang Z, Wong K.-Y.K (2012) Markov weight fields for face sketch synthesis, computer vision and pattern recognition (CVPR). In: 2012 IEEE Conference on IEEE, pp 1091–1097

  33. Wang N, Tao D, Gao X, Li X, Li J (2013) Transductive face sketch-photo synthesis. IEEE Trans Neural Netw Learn Syst 24:1364–1376

    Article  Google Scholar 

  34. Peng C, Gao X, Wang N, Li J (2017) Superpixel-based face sketch-photo synthesis. IEEE Trans Circuits Syst Video Technol 27:288–299

    Article  Google Scholar 

  35. Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Süsstrunk S (2012) SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34:2274–2282

    Article  Google Scholar 

  36. Wang N, Gao X, Sun L, Li J (2017) Bayesian face sketch synthesis. IEEE Trans Image Process 26:1264–1274

    Article  MathSciNet  MATH  Google Scholar 

  37. Zhang Y, McCullough C, Sullins JR, Ross CR (2010) Hand-drawn face sketch recognition by humans and a pca-based algorithm for forensic applications. IEEE Trans Syst Man Cyber Part A Syst Humans 40:475–485

    Article  Google Scholar 

  38. Klare BF, Jain AK (2013) Heterogeneous face recognition using kernel prototype similarities. IEEE Trans Pattern Anal Mach Intell 35:1410–1422

    Article  Google Scholar 

  39. Lahasan BM, Venkat I, Al-Betar MA, Lutfi SL, De Wilde P (2016) Recognizing faces prone to occlusions and common variations using optimal face subgraphs. Appl Math Comput 283:316–332

    MathSciNet  MATH  Google Scholar 

  40. Lahasan BM, Venkat I, Lutfi SL (2014) Recognition of occluded faces using an enhanced EBGM algorithm, computer and information sciences (ICCOINS). In: 2014 international conference on IEEE, pp 1–5

  41. Connolly J-F, Granger E, Sabourin R (2012) Evolution of heterogeneous ensembles through dynamic particle swarm optimization for video-based face recognition. Pattern Recognit 45:2460–2477

    Article  Google Scholar 

  42. Naik MK, Panda R (2016) A novel adaptive cuckoo search algorithm for intrinsic discriminant analysis based face recognition. Appl Soft Comput 38:661–675

    Article  Google Scholar 

  43. Al-Ayyoub M, AlZu’bi S, Jararweh Y, Shehab MA, Gupta BB (2016) Accelerating 3D medical volume segmentation using GPUs. Multim Tools Appl 77:1–20

    Google Scholar 

  44. Jararweh Y, Al-Ayyoub M, Fakirah M, Alawneh L, Gupta BB (2017) Improving the performance of the needleman–wunsch algorithm using parallelization and vectorization techniques. Multim Tools Appl 1–17. https://doi.org/10.1007/s11042-017-5092-0

  45. Alsmirat MA, Jararweh Y, Al-Ayyoub M, Shehab MA, Gupta BB (2017) Accelerating compute intensive medical imaging segmentation algorithms using hybrid CPU–GPU implementations. Multim Tools Appl 76:3537–3555

    Article  Google Scholar 

  46. Kennedy J, Eberhart R (1995) Particle swarm optimization, Neural Networks. IEEE Int Conf Proc 1944:1942–1948

    Google Scholar 

  47. Ratnaweera A, Halgamuge S, Watson HC (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evolut Comput 8:240–255

    Article  Google Scholar 

  48. Van Laarhoven PJ, Aarts EH (1987) Simulated annealing, simulated annealing: theory and applications. Springer, Berlin, pp 7–15

    Book  MATH  Google Scholar 

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

    Article  Google Scholar 

  50. Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28:2037–2041

    Article  MATH  Google Scholar 

  51. Liu C, Wechsler H (2002) Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans Image Process 11:467–476

    Article  Google Scholar 

  52. Huang GB, Ramesh M, Berg T, Learned-Miller E (2007) Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical report 07-49, University of Massachusetts, Amherst

  53. Martinez A, Benavente R (2007) The AR face database, 1998. Computer vision center, technical report, p 3

  54. Weiping C, Yongsheng G (2013) Face recognition using ensemble string matching. IEEE Trans Image Process 22:4798–4808

    Article  MathSciNet  MATH  Google Scholar 

  55. Gao Y, Qi Y (2005) Robust visual similarity retrieval in single model face databases. Pattern Recognit 38:1009–1020

    Article  Google Scholar 

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

    Article  Google Scholar 

  57. Efron B (1979) Bootstrap methods: another look at the Jackknife. Ann Stat 7:1–26

    Article  MathSciNet  MATH  Google Scholar 

  58. Efrorn B, Tibshirani RJ (1993) An Introduction to the bootstrap. Chapman and Hall, New York

    Book  Google Scholar 

  59. Van den Bergh F, Engelbrecht AP (2004) A Cooperative approach to particle swarm optimization. IEEE Trans Evolut Comput 8:225–239

    Article  Google Scholar 

  60. Pham D, Ghanbarzadeh A, Koc E, Otri S, Rahim S, Zaidi M (2005) The bees algorithm. Technical note, Manufacturing Engineering Centre, Cardiff University, UK, pp 1–57

  61. Zhao SZ, Suganthan PN, Pan Q-K, Fatih M (2011) Tasgetiren, Dynamic multi-swarm particle swarm optimizer with harmony search. Exp Syst Appl 38:3735–3742

    Article  Google Scholar 

  62. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  63. Mirjalili S, Mirjalili S, Hatamlou A (2015) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl, pp 1–19

  64. Sahu A, Panigrahi SK, Pattnaik S (2012) Fast convergence particle swarm optimization for functions optimization. Proc Technol 4:319–324

    Article  Google Scholar 

  65. Liu B, Wang L, Jin Y-H (2007) An effective PSO-based memetic algorithm for flow shop scheduling. IEEE Trans Syst Man Cyber Part B Cyber 37:18–27

    Article  Google Scholar 

  66. Rakshit P, Konar A, Bhowmik P, Goswami I, Das S, Jain LC, Nagar AK (2013) Realization of an adaptive memetic algorithm using differential evolution and Q-learning: a case study in multirobot path planning. IEEE Trans Syst Man Cyber Part B Cyber 43:814–831

    Article  Google Scholar 

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Acknowledgements

This paper was fully supported by Universiti Sains Malaysia (USM) Research University Individual (RUI) Grant Scheme under Grant Nos. 1001/PELECT/814208.

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Correspondence to Hussein Samma.

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Hussein Samma, Shahrel Azmin Suandi, and Junita Mohamad-Saleh declare that they have no conflict of interest.

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Samma, H., Suandi, S.A. & Mohamad-Saleh, J. Face sketch recognition using a hybrid optimization model. Neural Comput & Applic 31, 6493–6508 (2019). https://doi.org/10.1007/s00521-018-3475-4

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