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Proceedings Paper

Face detection and recognition using geometrical features and a neural network verifier
Author(s): Sung H. Yoon; Gi-yeon Park; Gi T. Hur; Jung H. Kim
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Paper 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.

Paper Details

Date Published: 17 April 2006
PDF: 9 pages
Proc. SPIE 6202, Biometric Technology for Human Identification III, 620205 (17 April 2006); doi: 10.1117/12.664756
Show Author Affiliations
Sung H. Yoon, North Carolina A&T State Univ. (United States)
Gi-yeon Park, North Carolina A&T State Univ. (United States)
Gi T. Hur, Dongshin Univ. (South Korea)
Jung H. Kim, North Carolina A&T State Univ. (United States)


Published in SPIE Proceedings Vol. 6202:
Biometric Technology for Human Identification III
Patrick J. Flynn; Sharath Pankanti, Editor(s)

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