Abstract
Facial recognition is one of the most efficient mechanisms to identify an individual by using facial attributes. Artificial Intelligence has been widely used to recognize facial attributes in different circumstances. Most facial recognition systems can analyze and compare various attributes of facial patterns to verify the identity of the person efficiently. Facial Recognition is the component of a biometric identification system. There have been many Deep learning algorithms proposed to extract the facial features, like Eigen Fisher faces which extract principal components and separate one from other. In the present scenario due to pandemics, people need to wear a mask which comes as a challenge as some of the facial features are not visible. However, the application of DNN helps to identify a person with the desired accuracy. If a person is wearing a mask or eyeglasses, in this case very few facial features are visible like eyebrows forehead, and shape. Many technologies help to identify the images, based on their features, color, shape, pose variation, expression changes, 2-Dimensional images, 3-Dimensional Images, RGB, and black and white images. In this paper, we present various Feature extraction and classification techniques and also compared the accuracy level of various facial recognitions methods on different databases.
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Shree, M., Dev, A., Mohapatra, A.K. (2023). Review on Facial Recognition System: Past, Present, and Future. In: Saraswat, M., Chowdhury, C., Kumar Mandal, C., Gandomi, A.H. (eds) Proceedings of International Conference on Data Science and Applications. Lecture Notes in Networks and Systems, vol 551. Springer, Singapore. https://doi.org/10.1007/978-981-19-6631-6_56
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