Abstract
Face recognition has a large extent of applications from individual recognizable proof and reconnaissance to electronics showcasing and publicizing for chosen clients. There are various advances in facial recognition, for example, pre-processing, feature extraction, and grouping, where feature extraction and grouping are utilized to acquire the greatest precision. In this paper, diverse feature extraction methods, for example, A.A.M, A.S.M, template-based, Gabor-features, and a few are basically surveyed. Aside from these, the various kinds of neural classification systems, for example, backpropagation, convolutional, radial-basis-function, and so on in the space of face recognition, are investigated. The method and calculations created in the present writing are examined, and it is uncovered that every system is one of a kind and has ideal execution. This assessment further makes a relative examination of these frameworks reliant on their focal points and imperatives.
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Rai, A., Shylaja, C., Mishra, P.K. (2022). Feature Extraction of Face Recognition Techniques Utilizing Neural System as a Classifier. In: Rao, V.V., Kumaraswamy, A., Kalra, S., Saxena, A. (eds) Computational and Experimental Methods in Mechanical Engineering. Smart Innovation, Systems and Technologies, vol 239. Springer, Singapore. https://doi.org/10.1007/978-981-16-2857-3_31
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