Robotic Facial Profile Identification using Neuro-Fuzzy System

Abstract

Facial recognition technology can verify or identify a person's identity. People can be recognized using facial recognition software in real-time or in images and videos. To identify the face, neural networks are trained to correctly classify the coefficients determined by the eigenface technique. The network is used to recognize the face photographs that are supplied to it after being trained on images from the face database. The basic difference between neural networks and fuzzy logic is that neural networks are mainly based on learning, help to perform predictions, and are difficult to extract knowledge from, while fuzzy logic isn't based on learning, helps to perform pattern recognition, and knowledge can easily be extracted. An artificial intelligence procedure called a neural network trains computers to examine evidence in a way similar to the human brain. Deep learning is a kind of machine learning method that uses networked nodes, or neurons, organized in a coated design to copy the associations of the human brain. A neuro-fuzzy system is an ambiguous system that applies data trials to govern its parameters (fuzzy rules and fuzzy sets) using a knowledge scheme inspired by or developed from neural artificial intelligence that merges fuzzy logic components with neural networks. One class of machine learning techniques used to model complicated patterns in data is neural networks. One kind of logic that permits approximate reasoning is fuzzy logic. Neuro-fuzzy systems are effective instruments for handling intricate, ambiguous, and ever-changing jobs. They combine the advantages of neural networks and fuzzy logic to attain exceptional outcomes.