International Journal of Advanced Innovative Technology in Engineering (IJAITE)

---

A Comparative Analysis for Selecting Multi-task Cascaded Convolutional Neural Networks (MTCNN) in Image Based Face Recognition System

Sachin Manohar Inwate, Dr. G. R. Bamnote, Prof. Y. S. Alone

Abstract :

Facial Recognition is a biometric software application capable of uniquely identifying or verifying a person by comparing and analyzing patterns, based on the person’s facial features. There are several reasons for recent increased interest in face recognition, including rising public concern for security, the need for identity verification in the digital world, face analysis and modelling techniques in multimedia data management and computer entertainment. Face recognition has recently gained significant attention, due to availability of feasible technologies, and advances in face modelling and analysis techniques. Although systems have been developed for face detection and tracking, reliable face recognition still offers a great challenge to computer vision and pattern recognition researchers. Face recognition process includes major components such as face detection, tracking, alignment and feature extraction, etc. In this paper, we have compared two face detection models, Multi-task Cascaded Convolutional Networks (MTCNN) and Viola and Jones Haar Cascades Model. This paper will comparatively analyze the result obtained through Haar Cascade and MTCNN and highlight the significance of MTCNN model over Haar-Cascade model while detecting a face from the captured photograph.

Keywords :

Full Text :

Download PDF

DOI :

Cite this paper :

References :

---