Abstract
In recent years a wide range of techniques have been developed to achieve accurate automated facial recognition. Ever since the success of the AlexNet neural network model based on convolutional neural networks (CNN) in the ImageNet competition in 2012, algorithms for object detection and recognition based on the so-called deep learning have attained significant improvements in performance. This success has inspired the implementation of similar models in facial recognition, resulting in vastly improved performance. As a result, recent research has for the most part been based on this paradigm. This chapter presents an overview of currently available deep neural network models in facial recognition. We outline the architectures and methods used by the best current models, and discuss performance issues related to the loss function, the optimization method and the choice of training dataset.
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Nzegha, A.F., Fendji, J.L.E., Thron, C., Tayou, C.D. (2020). Overview of Deep Learning in Facial Recognition. In: Subair, S., Thron, C. (eds) Implementations and Applications of Machine Learning. Studies in Computational Intelligence, vol 782. Springer, Cham. https://doi.org/10.1007/978-3-030-37830-1_6
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