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Machine Learning and Deep Learning Approaches: A Study for Face Recognition System

Moh’d Fatima Susilawati Binti, Darma Sanusi Abu

Abstract


For more than a decade, the face recognition system has been getting significant consideration in the field of biometric technology due to its frequent application in law enforcement, security systems, and different human-computer interactions. It is the biometric system for verifying and identifying humans or objects in a controlled environment. The challenging issues with face recognition systems are getting the right approaches for modeling face images. Face recognition methods are categorized into geometric or photometric approaches. The geometrics are those approaches that look for the characteristics of the face image. For example, the shapes and positions of the face's facial features. Whereas photometric methods are mathematical approaches that modify computer reference face images into values, by matching their shapes and positions with those of training images to reduce alterations for the purpose of face identification. In this paper, we investigate and discuss the most recent used machine learning and deep learning approaches and how these different approaches deal with those issues affecting the system in different existing literature works. The paper also highlights the background and origin of the face recognition system. Furthermore, we outline some of the application areas of face recognition systems.

Keywords


Learning, Deep learning Approaches Face Recognition, Biometric Authentication System

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