Face detection and recognition using geometrical features and a neural network verifier

Sung H. Yoon; Gi-yeon Park; Gi T. Hur; Jung H. Kim

doi:10.1117/12.664756

17 April 2006 Face detection and recognition using geometrical features and a neural network verifier

Sung H. Yoon, Gi-yeon Park, Gi T. Hur, Jung H. Kim

Proceedings Volume 6202, Biometric Technology for Human Identification III; 620205 (2006) https://doi.org/10.1117/12.664756
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

Abstract

This paper presents a new method for face verification for vision applications. There are many approaches to detect and track a face in a sequence of images; however, the high computations of image algorithms, as well as, face detection and head tracking failures under unrestricted environments remain to be a difficult problem. We present a robust algorithm that improves face detection and tracking in video sequences by using geometrical facial information and a recurrent neural network verifier. Two types of neural networks are proposed for face detection verification. A new method, a three-face reference model (TFRM), and its advantages, such as, allowing for a better match for face verification, will be discussed in this paper.

Citation Download Citation

Sung H. Yoon, Gi-yeon Park, Gi T. Hur, and Jung H. Kim "Face detection and recognition using geometrical features and a neural network verifier", Proc. SPIE 6202, Biometric Technology for Human Identification III, 620205 (17 April 2006); https://doi.org/10.1117/12.664756

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available