Abstract
Recently, in the field of face recognition, deep learning has achieved great success. FaceNet, one of the Google’s deep learning frameworks, obtained nearly 100% accuracy in recognizing high-resolution faces. But, its performance on low-resolution images is unsatisfactory. Here, to justify the above statement, the performance of FaceNet has been evaluated on four well-known facial databases. For improving the performance, the face recognition algorithm for low-resolution images has been designed. Specifically, the algorithm performs image enhancement using a customized error back propagation network, and then the nearest patterns (NP) have been extracted and fed as embeddings to FaceNet. NP is a modified version of dual-cross patterns face descriptor that encodes the texture information of each image pixel in eight directions as unique features. The experimentation done on various databases such as LFW, ORL, EYB, and AR. The results of proposed approaches with NP have shown better performance than the other face descriptors like DCP, LBP, and LTP. Moreover, better results have been obtained on shrinking the Inception-Resnet-v1 network, which resulted in the fast convergence of the FaceNet.
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Golla, M.R., Sharma, P., Madarkar, J. (2020). Face Recognition Algorithm for Low-Resolution Images. In: Shukla, R., Agrawal, J., Sharma, S., Chaudhari, N., Shukla, K. (eds) Social Networking and Computational Intelligence. Lecture Notes in Networks and Systems, vol 100. Springer, Singapore. https://doi.org/10.1007/978-981-15-2071-6_29
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DOI: https://doi.org/10.1007/978-981-15-2071-6_29
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