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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yi, S., Lai, Z., He, Z., Cheung, Y.-M., Liu, Y.: Joint sparse principal component analysis. Pattern Recogn. 61, 524–536 (2017)
Zafeiriou, S., Petrou, M.: 2.5 D elastic graph matching. Comput. Vis. Image Underst. 115(7), 1062–1072 (2011)
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)
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
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)
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)
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)
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)
Matthews, I., Baker, S.: Active appearance models revisited. Int. J. Comput. Vis. 60(2), 135–164 (2004)
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)
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)
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)
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
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)
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)
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)
Saraswat, M., Arya, K.: Feature selection and classification of leukocytes using random forest. Med. Biol. Eng. Comput. 52, 1041–1052 (2014)
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)
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
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
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)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
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)
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)
Hussain, K., Salleh, M.N.M., Cheng, S., Shi, Y.: Metaheuristic research: a comprehensive survey. Artif. Intell. Rev. 2018, 1–43 (2018)
Saraswat, M., Arya, K., Sharma, H.: Leukocyte segmentation in tissue images using differential evolution algorithm. Swarm Evol. Comput. 11, 46–54 (2013)
Bansal, J.C., Sharma, H., Jadon, S.S., Clerc, M.: Spider monkey optimization algorithm for numerical optimization. Memetic Comput. 6(1), 31–47 (2014)
Mohammadi, F.G., Abadeh, M.S.: Image steganalysis using a bee colony-based feature selection algorithm. Eng. Appl. Artif. Intell. 31, 35–43 (2014)
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)
Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179, 2232–2248 (2009)
Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110, 346–359 (2008)
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)
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)
ORL database of face images, September 2018. https://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-981-13-9942-8_49
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9941-1
Online ISBN: 978-981-13-9942-8
eBook Packages: Computer ScienceComputer Science (R0)