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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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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