Biometric Facial Detection and Recognition Based on ILPB and SVM
Shubhi Srivastava, Ankit Kumar, Shiv Prakash · 2021
Biometric security has long been a trending zone that satisfies the need for a significant level of security and control. Among all the existing technologies, face detection is one of the most utilized and adjusted innovations. The identification failure of a user's identity is a big concern. In this chapter, a novel approach for biometric recognition has been introduced in which the application of ILBP (Improved Local Binary Pattern) for facial feature detection is discussed which generates improved features for the facial pattern. It allows only an authenticated user to access a system, which is better than previous algorithms. Previous research for face detection shows many demerits in terms of false acceptance and rejection rates. In this paper, the extraction of Facial features is done from static and dynamic frames using the Haar cascade algorithm. Then, the ILBP method which works on local pixel values of an image is applied for feature extraction, and finally, the SVM (support vector machine) is used for classification of those features. The objective of this paper is to provide the best recognition results from images that are taken randomly and may possess noise. This paper achieved an accuracy of 97.90% for correct recognition and with less time complexity. It can be used in crime investigation, security cameras, digital forensics, etc.