Sustainable Facial Authentication and Expression Prediction using Deep Learning Techniques

¹ Department of Information Technology, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, Telangana, India
² School of Applied and Life Sciences, Uttaranchal University, Dehradun, 248007, India

^* Corresponding author: rajasekhar1581@grietcollege.com

Abstract

To make machines more human-like, computer vision is essential. Computer vision is a field that focuses on mimicking the capabilities of the human visual framework to capture high-level understanding from enhanced images or recordings. Such a computer vision application gives the machine the ability to recognize a person and detect their emotions in order to process them appropriately. Facial verification can be a process of identifying or validating an object through an image, video, or any audio-visual component of their face. It could be a biometric discriminant proof strategy that works directly and confronts measures of discriminating individuals through their facial design and biometric information. The innovation collects a unique set of biometric information from each individual regarding their face and facial expressions to identify an individual. Facial sensory recognition can be an innovation used to analyse estimates from a variety of sources, such as images and recordings. It makes a difference as machines get better the way humans do and treat them according to their emotions. We are using a deep learning computation called Convolutional Neural Networks (CNN) to prepare for this demonstration that determines the sentiment of certain input images. We need to pre-process the images to prepare and test the model. For pre-processing, we do image enhancement combining resizing, equalizing and converting the image to grayscale for the machine to achieve. This demonstration can have multiple applications in both surveillance and feedback systems.