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Enhanced Bag-of-Features Method Using Grey Wolf Optimization for Automated Face Retrieval

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Abstract

As images are increasing exponentially over the Internet, the retrieval of such images using content-based approach becomes an important research area. Out of the various models of the image retrievals, recognition of facial images is highly used by many application areas. However, due to the different variations involved in the facial images, it is a challenging problem. Therefore, this work introduces an efficient face recognition method which uses the bag-of-features approach for the same. The proposed bag-of-features based face recognition approach uses Grey wolf optimization algorithm for obtaining the prominent visual words. The enhanced bag-of-features based face recognition approach has been analyzed on a face database of Oracle Research Laboratory against the classification accuracy. The experimental results show that the presented method identifies the faces more accurately than the other meta-heuristic based approaches.

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References

  1. Yi, S., Lai, Z., He, Z., Cheung, Y.-M., Liu, Y.: Joint sparse principal component analysis. Pattern Recogn. 61, 524–536 (2017)

    Article  Google Scholar 

  2. Zafeiriou, S., Petrou, M.: 2.5 D elastic graph matching. Comput. Vis. Image Underst. 115(7), 1062–1072 (2011)

    Article  Google Scholar 

  3. Senaratne, R., Halgamuge, S., Hsu, A.: Face recognition by extending elastic bunch graph matching with particle swarm optimization. J. Multimed. 4(4), 204–214 (2009)

    Article  Google Scholar 

  4. Wiskott, L., Fellous, J.-M., Krüger, N., von der Malsburg, C.: Face recognition by elastic bunch graph matching. In: Sommer, G., Daniilidis, K., Pauli, J. (eds.) CAIP 1997. LNCS, vol. 1296, pp. 456–463. Springer, Heidelberg (1997). https://doi.org/10.1007/3-540-63460-6_150

    Chapter  Google Scholar 

  5. Liu, C., Wechsler, H.: Enhanced fisher linear discriminant models for face recognition. In: 1998 Proceedings of Fourteenth International Conference on Pattern Recognition, vol. 2, pp. 1368–1372. IEEE (1998)

    Google Scholar 

  6. Lin, C., Long, F., Zhan, Y.: Facial expression recognition by learning spatiotemporal features with multi-layer independent subspace analysis. In: 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp. 1–6. IEEE (2017)

    Google Scholar 

  7. Lu, J., Wang, G., Zhou, J.: Simultaneous feature and dictionary learning for image set based face recognition. IEEE Trans. Image Process. 26(8), 4042–4054 (2017)

    Article  MathSciNet  Google Scholar 

  8. Ding, C., Tao, D.: Trunk-branch ensemble convolutional neural networks for video-based face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40, 1002–1014 (2017)

    Article  MathSciNet  Google Scholar 

  9. Matthews, I., Baker, S.: Active appearance models revisited. Int. J. Comput. Vis. 60(2), 135–164 (2004)

    Article  Google Scholar 

  10. Besbas, W., Artemi, M., Salman, R.: Content based image retrieval (CBIR) of face sketch images using WHT transform domain. Inform. Environ. Energy Appl. 66, 77–81 (2014)

    Google Scholar 

  11. Shih, P., Liu, C.: Comparative assessment of content-based face image retrieval in different color spaces. Int. J. Pattern Recognit. Artif. Intell. 19(07), 873–893 (2005)

    Article  Google Scholar 

  12. ElAdel, A., Ejbali, R., Zaied, M., Amar, C.B.: A hybrid approach for content-based image retrieval based on fast beta wavelet network and fuzzy decision support system. Mach. Vis. Appl. 27(6), 781–799 (2016)

    Article  Google Scholar 

  13. Desai, R., Sonawane, B.: GIST, HOG, and DWT-based content-based image retrieval for facial images. In: Satapathy, S., Bhateja, V., Joshi, A. (eds.) Proceedings of the International Conference on Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol. 468, pp. 297–307. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-1675-2_31

    Chapter  Google Scholar 

  14. Sultana, M., Gavrilova, M.L.: Face recognition using multiple content-based image features for biometric security applications. Int. J. Biometr. 6(4), 414–434 (2014)

    Article  Google Scholar 

  15. Wang, X.-Y., Liang, L.-L., Li, Y.-W., Yang, H.-Y.: Image retrieval based on exponent moments descriptor and localized angular phase histogram. Multimed. Tools Appl. 76(6), 7633–7659 (2017)

    Article  Google Scholar 

  16. Wu, Z., Ke, Q., Sun, J., Shum, H.Y.: Scalable face image retrieval with identity-based quantization and multi-reference re-ranking. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3469–3476. IEEE (2010)

    Google Scholar 

  17. Saraswat, M., Arya, K.: Feature selection and classification of leukocytes using random forest. Med. Biol. Eng. Comput. 52, 1041–1052 (2014)

    Article  Google Scholar 

  18. Xu, J., et al.: Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans. Med. Imaging 35(1), 119–130 (2016)

    Article  MathSciNet  Google Scholar 

  19. Chang, H., Nayak, N., Spellman, P.T., Parvin, B.: Characterization of tissue histopathology via predictive sparse decomposition and spatial pyramid matching. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 91–98. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40763-5_12

    Chapter  Google Scholar 

  20. Cruz-Roa, A.A., Arevalo Ovalle, J.E., Madabhushi, A., González Osorio, F.A.: A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 403–410. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40763-5_50

    Chapter  Google Scholar 

  21. Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)

    Article  Google Scholar 

  22. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  23. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)

    Google Scholar 

  24. Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV, Prague, vol. 1, no. 1–22, pp. 1–2 (2004)

    Google Scholar 

  25. Hussain, K., Salleh, M.N.M., Cheng, S., Shi, Y.: Metaheuristic research: a comprehensive survey. Artif. Intell. Rev. 2018, 1–43 (2018)

    Google Scholar 

  26. Saraswat, M., Arya, K., Sharma, H.: Leukocyte segmentation in tissue images using differential evolution algorithm. Swarm Evol. Comput. 11, 46–54 (2013)

    Article  Google Scholar 

  27. Bansal, J.C., Sharma, H., Jadon, S.S., Clerc, M.: Spider monkey optimization algorithm for numerical optimization. Memetic Comput. 6(1), 31–47 (2014)

    Article  Google Scholar 

  28. Mohammadi, F.G., Abadeh, M.S.: Image steganalysis using a bee colony-based feature selection algorithm. Eng. Appl. Artif. Intell. 31, 35–43 (2014)

    Article  Google Scholar 

  29. Chhikara, R.R., Sharma, P., Singh, L.: A hybrid feature selection approach based on improved PSO and filter approaches for image steganalysis. Int. J. Mach. Learn. Cybernet. 7, 1195–1206 (2016)

    Article  Google Scholar 

  30. Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179, 2232–2248 (2009)

    Article  Google Scholar 

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

    Article  Google Scholar 

  32. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110, 346–359 (2008)

    Article  Google Scholar 

  33. Ali Bagheri, M., Montazer, G.A., Escalera, S.: Error correcting output codes for multiclass classification: application to two image vision problems. In: 2012 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP), pp. 508–513. IEEE (2012)

    Google Scholar 

  34. Jiang, Y.-G., Yang, J., Ngo, C.-W., Hauptmann, A.G.: Representations of keypoint-based semantic concept detection: A comprehensive study. IEEE Trans. Multimed. 12(1), 42–53 (2010)

    Article  Google Scholar 

  35. ORL database of face images, September 2018. https://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html

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Correspondence to Arun Kumar Shukla .

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Shukla, A.K., Kanungo, S. (2019). Enhanced Bag-of-Features Method Using Grey Wolf Optimization for Automated Face Retrieval. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Kashyap, R. (eds) Advances in Computing and Data Sciences. ICACDS 2019. Communications in Computer and Information Science, vol 1046. Springer, Singapore. https://doi.org/10.1007/978-981-13-9942-8_49

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  • DOI: https://doi.org/10.1007/978-981-13-9942-8_49

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