Title: A hybrid approach for face recognition using LBP and multi level classifier

Authors: Mukesh Kumar Gupta; Pankaj Dadheech; Ankit Kumar; Ashok Kumar Saini; Neha Janu; Sanwta Ram Dogiwal

Addresses: Swami Keshvanand Institute of Technology, Management and Gramothan (SKIT), Ramnagaria, Jagatpura, Jaipur, Rajasthan-302017, India ' Swami Keshvanand Institute of Technology, Management and Gramothan (SKIT), Ramnagaria, Jagatpura, Jaipur, Rajasthan-302017, India ' GLA University, NH-2, Mathura-Delhi Road, Mathura, Chaumuhan, UP-281406, India ' JECRC University, Plot No. IS-2036 to IS-2039, Ramchandrapura Industrial Area Jaipur, Sitapura, Vidhani, Jaipur, Rajasthan-303905, India ' Swami Keshvanand Institute of Technology, Management and Gramothan (SKIT), Ramnagaria, Jagatpura, Jaipur, Rajasthan-302017, India ' Swami Keshvanand Institute of Technology, Management and Gramothan (SKIT), Ramnagaria, Jagatpura, Jaipur, Rajasthan-302017, India

Abstract: General face recognition, a task performed by humans in daily activities, is derived from a virtually uncontrolled environment. This paper presents a facial recognition system based on random forest and support vector machine. When compared to previous methods, this approach achieves high accuracy. In this paper, we proposed a hybrid method using SVM and random forest classification. The RF+SVM method predicts rapid growth in popularity. This combined method aids in high recognition speed with a wide range of faces and emotions. We also compared the algorithm to previous techniques. Each experiment made use of a free internet database. In the experiment, 400 photographs of 40 people are used. The reason for the improved results in this paper's hybrid vehicle classification methodology is that it combines the advantages of both traditional SVM and RF class methods. The proposed system has an accuracy of 98.6%.

Keywords: biometrics; database; face recognition; SVM classifier; random forest.

DOI: 10.1504/IJBM.2023.130640

International Journal of Biometrics, 2023 Vol.15 No.3/4, pp.359 - 388

Received: 15 Jul 2021
Accepted: 30 Nov 2021
Published online: 02 May 2023 *

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