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An Automated Face Retrieval System Using Grasshopper Optimization Algorithm-Based Feature Selection Method

  • Arun Kumar ShuklaEmail author
  • Suvendu Kanungo
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 35)

Abstract

Facial image retrieval using its contents is one of the major areas of research because of the exponential increase of multimedia data over the Internet. However, due to high dimensional features and different variations available in the images, it becomes a challenging task to obtain the relevant and non-redundant features. Therefore, for making the facial retrieval system more accurate and computationally efficient, the selection of prominent features is an important phase. In this paper, the grasshopper optimization algorithm has been used to obtain the relevant attributes from the high dimensional features vector. For the same, Oracle Research Laboratory database of faces is used. The experimental values show the efficacy of the proposed method of feature selection which eliminates the maximum 83% features among the other considered methods and the accuracy of the facial retrieval system also increases to 91.5%.

Keywords

Face retrieval system Feature selection Clustering Grasshopper optimization algorithm 

References

  1. 1.
    Zafeiriou, S., Petrou, M.: 2.5 D elastic graph matching. Comput. Vis. Image Underst. 115(7), 1062–1072 (2011)CrossRefGoogle Scholar
  2. 2.
    Senaratne, R., Halgamuge, S., Hsu, A.: Face recognition by extending elastic bunch graph matching with particle swarm optimization. J. Multimedia 4, 204–214 (2009)CrossRefGoogle Scholar
  3. 3.
    Cooper, H., Ong, E.-J., Pugeault, N., Bowden, R.: Sign language recognition using sub-units. In: Gesture Recognition, pp. 89–118. Springer (2017)Google Scholar
  4. 4.
    Yi, S., Lai, Z., He, Z., Cheung, Y.-M., Liu, Y.: Joint sparse principal component analysis. Pattern Recogn. 61, 524–536 (2017)CrossRefGoogle Scholar
  5. 5.
    Liu, C., Wechsler, H.: Enhanced fisher linear discriminant models for face recognition. In: Proceedings of the Fourteenth International Conference on Pattern Recognition 1998, vol. 2, pp. 1368–1372. IEEE (1998)Google Scholar
  6. 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. 7.
    Ding, C., Tao, D.: Trunk-branch ensemble convolutional neural networks for video-based face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 1002–1014 (2017)MathSciNetCrossRefGoogle Scholar
  8. 8.
    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)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Saraswat, M., Arya, K.: Automatic facial expression recognition in an image sequence of non-manual indian sign language using support vector machine. In: Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011), 20–22 December 2011, pp. 267–275. Springer (2012)Google Scholar
  10. 10.
    Saraswat, M., Arya, K.: Automatic facial landmark detection in a video sequences of non-manual sign languages. In: International Conference on Industrial and Information Systems (ICIIS), 2009, pp. 358–361. IEEE (2009)Google Scholar
  11. 11.
    Saraswat, M., Arya, K.: Feature selection and classification of leukocytes using random forest. Med. Biol. Eng. Comput. 52, 1041–1052 (2014)CrossRefGoogle Scholar
  12. 12.
    Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. 1, 131–156 (1997)CrossRefGoogle Scholar
  13. 13.
    Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46, 389–422 (2002)CrossRefGoogle Scholar
  14. 14.
    Deng, H., Runger, G.: Feature selection via regularized trees. In: Proceedings of International Joint Conference on Neural Networks, pp. 1–8 (2012)Google Scholar
  15. 15.
    Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97, 273–324 (1997)CrossRefGoogle Scholar
  16. 16.
    Pal, R., Saraswat, M.: Data clustering using enhanced biogeography-based optimization. In: Tenth International Conference on Contemporary Computing (IC3), 2017, pp. 1–6. IEEE (2017)Google Scholar
  17. 17.
    Pal, R., Pandey, H.M.A., Saraswat, M.: BEECP: biogeography optimization-based energy efficient clustering protocol for HWSNs. In: 2016 Ninth International Conference on Contemporary Computing (IC3), pp. 1–6. IEEE (2016)Google Scholar
  18. 18.
    Pandey, A.C., Rajpoot, D.S., Saraswat, M.: Data clustering using hybrid improved cuckoo search method. In: 2016 Ninth International Conference on Contemporary Computing (IC3), pp. 1–6. IEEE (2016)Google Scholar
  19. 19.
    Saraswat, M., Arya, K., Sharma, H.: Leukocyte segmentation in tissue images using differential evolution algorithm. Swarm Evol. Comput. 11, 46–54 (2013)CrossRefGoogle Scholar
  20. 20.
    Pandey, A.C., Rajpoot, D.S., Saraswat, M.: Twitter sentiment analysis using hybrid cuckoo search method. Inf. Process. Manag. 53(4), 764–779 (2017)CrossRefGoogle Scholar
  21. 21.
    Mittal, H., Saraswat, M.: Classification of histopathological images through bag-of-visual-words and gravitational search algorithm. In: Soft Computing for Problem Solving, pp. 231–241. Springer (2019)Google Scholar
  22. 22.
    Kulhari, A., Saraswat, M.: Differential evolution-based subspace clustering via thresholding ridge regression. In: 2017 Tenth International Conference on Contemporary Computing (IC3), pp. 1–3. IEEE (2017)Google Scholar
  23. 23.
    Gupta, M., Parmar, G., Gupta, R., Saraswat, M.: Discrete wavelet transform-based color image watermarking using uncorrelated color space and artificial bee colony. Int. J. Comput. Intell. Syst. 8(2), 364–380 (2015)CrossRefGoogle Scholar
  24. 24.
    Mittal, H., Saraswat, M.: An optimum multi-level image thresholding segmentation using non-local means 2D histogram and exponential Kbest gravitational search algorithm. Eng. Appl. Artif. Intell. 71, 226–235 (2018)CrossRefGoogle Scholar
  25. 25.
    Mittal, H., Saraswat, M.: An image segmentation method using logarithmic kbest gravitational search algorithm based superpixel clustering. Evol. Intell. 1–13 (2018)Google Scholar
  26. 26.
    Pal, R., Saraswat, M.: Enhanced bag of features using AlexNet and improved biogeography-based optimization for histopathological image analysis. In: 2018 Eleventh International Conference on Contemporary Computing (IC3), pp. 1–6. IEEE (2018)Google Scholar
  27. 27.
    Mittal, H., Saraswat, M.: An automatic nuclei segmentation method using intelligent gravitational search algorithm based superpixel clustering. Swarm Evol. Comput. 45, 15–32 (2019)CrossRefGoogle Scholar
  28. 28.
    Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)CrossRefGoogle Scholar
  29. 29.
    Pandey, A.C., Rajpoot, D.S., Saraswat, M.: Hybrid step size based cuckoo search. In: 2017 Tenth International Conference on Contemporary Computing (IC3), pp. 1–6. IEEE (2017)Google Scholar
  30. 30.
    Sharma, H., Hazrati, G., Bansal, J.C.: Spider monkey optimization algorithm. In: Evolutionary and Swarm Intelligence Algorithms, pp. 43–59. Springer (2019)Google Scholar
  31. 31.
    Mirjalili, S.: SCA: a sine cosine algorithm for solving optimization problems. Knowl.-Based Syst. 96, 120–133 (2016)CrossRefGoogle Scholar
  32. 32.
    Saremi, S., Mirjalili, S., Lewis, A.: Grasshopper optimisation algorithm: theory and application. Adv. Eng. Softw. 105, 30–47 (2017)CrossRefGoogle Scholar
  33. 33.
    Lukasik, S., Kowalski, P.A., Charytanowicz, M., Kulczycki, P.: Data clustering with grasshopper optimization algorithm. In: Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 71–74. IEEE (2017)Google Scholar
  34. 34.
    Tharwat, A., Houssein, E.H., Ahmed, M.M., Hassanien, A.E., Gabel, T.: MOGOA algorithm for constrained and unconstrained multi-objective optimization problems. Appl. Intell. 48(8), 2268–2283 (2018)CrossRefGoogle Scholar
  35. 35.
    Barman, M., Choudhury, N.D., Sutradhar, S.: A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam, India. Energy 145, 710–720 (2018)CrossRefGoogle Scholar
  36. 36.
    Ibrahim, H.T., Mazher, W.J., Ucan, O.N., Bayat, O.: A grasshopper optimizer approach for feature selection and optimizing SVM parameters utilizing real biomedical data sets. Neural Comput. Appl. 1–10Google Scholar
  37. 37.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  38. 38.
    Krig, S.: Feature learning and deep learning architecture survey. In: Computer Vision Metrics, pp. 375–514. Springer (2016)Google Scholar
  39. 39.
    Prijono, B.: Student notes: convolutional neural networks (CNN) introduction (2018). https://indoml.com/2018/03/07/student-notes-convolutional-neural-networks-cnn-introduction. Accessed 09 June 2018
  40. 40.
    ORL database of face images, September 2018. https://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringBirla Institute of Technology, Mesra, RanchiAllahabadIndia

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