A Real Time Employee Attendance Monitoring System using ANN
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Abstract
Face recognition refers to the technology that examines and contrasts a person's face characteristics to recognise or verify their identity. Recently, this technology has drawn a lot of attention due to the potential uses it may have in security, marketing, and law enforcement. Face recognition involves studying a picture or video of a person's face to identify features like the space between their eyes, the contour of their nose, and the curve of their mouth. The person's identity is then established or verified by comparing these characteristics to a database of previously saved pictures. A series of techniques called facial recognition algorithms are used to identify and authenticate persons based on the features of their faces. These algorithms compare a person's facial attributes to those in a database of recognised faces by looking at things like the shape of their face, the distance between their eyes, and other distinctive facial features. There are many different types of face recognition algorithms, including geometric-based algorithms, appearance-based algorithms, and hybrid algorithms that combine both approaches. Geometric-based algorithms employ the geometry of face traits to identify and validate people, while appearance-based algorithms use image processing techniques to compare the patterns and textures of facial features. Recent advances in deep learning have significantly improved the accuracy of facial recognition algorithms. Artificial Neural Network (ANN) has shown to be highly effective and have been used in a range of applications, including mobile devices, security, and surveillance. Face recognition algorithms provide advantages, but there are also moral dilemmas with regard to its application, such as potential biases and privacy difficulties. As technology advances, it is imperative to address these problems and ensure that face recognition algorithms are used ethically and responsibly.
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