Abstract
Facial keypoints (FKP) detection is considered as a challenging task in the field of computer vision, as facial features vary from individual to individual. It becomes a more challenging proposition as the same person facial image may also vary due to change in position, size, pose, expression etc. Some methods exist in literature for detection of FKPs. In this paper, a deep architecture is used to locate the keypoints on gray-scale images. As baseline method one hidden layer neural network and convolutional neural networks are built in the proposed work. Additionally, a block of pretrained Inception module is used to extract the intermediate features. Specifically, the sparse structure of Inception model reduces the computational cost of the proposed method significantly. The methods are evaluated on standard dataset and compared with existing state-of-the-art CNN based methods. The obtained results are promising and also bring out the efficiency of the proposed work.
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Dasgupta, M., Mandal, J.K. (2019). Deep Convolutional Neural Network Based Facial Keypoints Detection. In: Mandal, J., Mukhopadhyay, S., Dutta, P., Dasgupta, K. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2018. Communications in Computer and Information Science, vol 1030. Springer, Singapore. https://doi.org/10.1007/978-981-13-8578-0_4
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