Detection, Segmentation and Recognition of Face and its Features Using Neural Network

TL;DR

This paper reviews face detection and recognition techniques, emphasizing neural networks' advantages over traditional methods in accuracy and efficiency, especially using backpropagation for nonlinear face recognition.

Contribution

It highlights the effectiveness of neural networks, particularly backpropagation, in improving face recognition accuracy and speed over traditional PCA, ICA, and LDA methods.

Findings

Neural networks achieved over 90% recognition accuracy.

Backpropagation enabled fast recognition within seconds.

Neural approaches outperformed traditional PCA, ICA, LDA methods.

Abstract

Face detection and recognition has been prevalent with research scholars and diverse approaches have been incorporated till date to serve purpose. The rampant advent of biometric analysis systems, which may be full body scanners, or iris detection and recognition systems and the finger print recognition systems, and surveillance systems deployed for safety and security purposes have contributed to inclination towards same. Advances has been made with frontal view, lateral view of the face or using facial expressions such as anger, happiness and gloominess, still images and video image to be used for detection and recognition. This led to newer methods for face detection and recognition to be introduced in achieving accurate results and economically feasible and extremely secure. Techniques such as Principal Component analysis (PCA), Independent component analysis (ICA), Linear Discriminant Analysis (LDA), have been the predominant ones to be used. But with improvements needed in the previous approaches Neural Networks based recognition was like boon to the industry. It not only enhanced the recognition but also the efficiency of the process. Choosing Backpropagation as the learning method was clearly out of its efficiency to recognize nonlinear faces with an acceptance ratio of more than 90% and execution time of only few seconds.