Abstract
Over the years, quite a lot of research has been done on using different machine learning techniques for Face Recognition (FR) for identifying the faces of different people. Current FR techniques are still not accurate enough for real world scenarios and pose a lot of problems against varying illumination levels, pose variations, noise and occlusion in the image. Thus, a single keypoint extraction technique may not be suitable for all cases. Hence, in this paper a novel technique is proposed for Keypoint Fusion (KF) obtained by fusing SIFT, SURF and ORB keypoints which is more accurate and suitable for real time application. The paper is also focused on proposing a novel technique of using a Self-Organizing Map (SOM) and Vector of Locally Aggregated Descriptors (VLAD) for image clustering. VLAD is used to extend the SOM’s ability to cluster keypoint descriptors. Image classification is carried out using a SGD (Stochastic Gradient Descent) based SVM (Support Vector Machine) classifier. The performance of classification of the proposed framework on benchmark datasets (Grimace, Faces95 and Faces96) has been tabulated and compared with other standard techniques. It is seen that the proposed framework performs better than the BOW (Bag Of Words) model and the KF technique was accurate and quick enough to beat the traditional keypoint extraction techniques.
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References
Lei, Y., Bennamoun, M., El-Sallam, A.A.: An efficient 3D face recognition approach based on the fusion of novel local low-level features. Pattern Recogn. 46(1), 24–37 (2013)
Tan, X., Triggs, B.: Fusing gabor and LBP feature sets for Kernel-based face recognition. In: Zhou, S.K., Zhao, W., Tang, X., Gong, S. (eds.) AMFG 2007. LNCS, vol. 4778, pp. 235–249. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75690-3_18
Geng, C., Jiang, X.: Face recognition using sift features. In: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 3313–3316. IEEE (2009)
Jurie, F., Schmid, C.: Scale-invariant shape features for recognition of object categories. In: CVPR, vol. II, pp. 90–96 (2004)
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). https://doi.org/10.1007/11744023_32
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)
Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2564–2571. IEEE (2011)
Lowe, D.G.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157. IEEE (1999)
Kohonen, T.: Self-Organizing Maps. SSINF. Springer, Heidelberg (1995). https://doi.org/10.1007/978-3-642-97610-0
Kohonen, T.: The self-organizing map. Proc. IEEE 78(9), 1464–1480 (1990)
Arandjelovic, R., Zisserman, A.: All about VLAD. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1578–1585 (2013). https://doi.org/10.1109/CVPR.2013.207
Jegou, H., Perronnin, F., Douze, M., Sanchez, J., Perez, P., Schmid, C.: Aggregating local image descriptors into compact codes. IEEE Trans. Pattern Anal. Mach. Intell. 34, 1704 (2012)
The Faces95 database. http://cswww.essex.ac.uk/mv/allfaces/faces95.html
The Faces96 database. http://cswww.essex.ac.uk/mv/allfaces/faces96.html
The Grimace database. http://cswww.essex.ac.uk/mv/allfaces/grimace.html
Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Lechevallier, Y., Saporta, G. (eds.) Proceedings of COMPSTAT 2010, pp. 177–186. Physica, Heidelberg (2010). https://doi.org/10.1007/978-3-7908-2604-3_16
Candemir, S., Borovikov, E., Santosh, K., Antani, S., Thoma, G.: RSILC: rotation- and scale-invariant, line-based color-aware descriptor. Image Vis. Comput. 42, 1–12 (2015)
Sawat, D., Hegadi, R.: Unconstrained face detection: a deep learning and machine learning combined approach. CSI Trans. ICT. 5, 1–5 (2016). https://doi.org/10.1007/s40012-016-0149-1
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Vinay, A., Cholin, A.S., Bhat, A.D., Deshpande, A.A., Murthy, K.N.B., Natarajan, S. (2019). SOM-VLAD Based Feature Aggregation for Face Recognition Using Keypoint Fusion. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1036. Springer, Singapore. https://doi.org/10.1007/978-981-13-9184-2_46
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DOI: https://doi.org/10.1007/978-981-13-9184-2_46
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