Abstract
Face recognition technologies must be able to recognize users’ faces in a chaotic environment. Facial detection is a different issue from facial recognition in that it requires reporting the position and size of every face in an image, whereas facial recognition does not allow for this. Due to their general similarity in look, the photographs of the same face have several alterations, which makes it a challenging challenge to solve. Face recognition is an extremely challenging process to do in an uncontrolled environment because the lighting, perspective, and quality of the image to be identified all have a significant impact on the process's output. The paper proposed a hybrid model for the face recognition using machine learning. Their performance is calculated on the basis of value derived for the FAR, FRR, TSR, ERR. At the same time their performance is compared with some existing machine learning model. It was found that the proposed hybrid model achieved the accuracy of almost 98%.
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Poonia, R.C., Samanta, D., Prabu, P. (2023). Design and Implementation of Machine Learning-Based Hybrid Model for Face Recognition System. In: So-In, C., Londhe, N.D., Bhatt, N., Kitsing, M. (eds) Information Systems for Intelligent Systems . Smart Innovation, Systems and Technologies, vol 324. Springer, Singapore. https://doi.org/10.1007/978-981-19-7447-2_6
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DOI: https://doi.org/10.1007/978-981-19-7447-2_6
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