Abstract
In this paper, we propose a novel face recognition pipeline based on keypoint detection and classification. The proposed approach makes use of the recent advances in the field of deep learning through a recently proposed keypoint detector and descriptor called learned invariant feature transform (LIFT). We also incorporate extreme learning machines (ELMs) for the purpose of classification. The descriptors are aggregated using vector of locally aggregated descriptors (VLADs). This approach is tested extensively in comparison with other well-known descriptors on databases like ORL, Faces94, and Grimace and has been proved to outperform other descriptors in most cases. This provides a fast and accurate algorithm for face recognition.
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Vinay, A., S. Hegde, N., Tejas, S.K., Patil, N.V., Natarajan, S., Balasubramanya Murthy, K.N. (2019). Learned Invariant Feature Transform and Extreme Learning Machines for Face Recognition. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 817. Springer, Singapore. https://doi.org/10.1007/978-981-13-1595-4_23
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