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

Neural networks

Neurons

Tolerancing

Eye models

Detection and tracking algorithms

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