Human Face Extraction and Recognition Using Radial Basis Function Networks

Kiminori SATO; Nan HE; Yukitoshi TAKAHASHI

Human Face Extraction and Recognition Using Radial Basis Function Networks

Kiminori SATO, Nan HE, Yukitoshi TAKAHASHI

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Partial face images, e.g., eyes, nose, and ear images are significant for face recognition. In this paper, we present a method for partial face extraction and recognition based on Radial Basis Function (RBF) networks. Focus has been centered on using ear images because they are not influenced by facial expression, and the influences of aging are negligible. Original human side face image with 320240 pixels is input, and then the RBF network locates the ear and extracts it with a 200120 pixel image. Next, another RBF network is constructed for the purpose of recognition. An algorithm that determines the radius of an RBF function is proposed. Dynamic radius, so called as compared to static one, is found through the algorithm that makes RBF functions adaptable to the training samples. We built a database that contains 600 side face images, from 100 people, to test the method and the results of both extraction and recognition are satisfied.

Publication: IEICE TRANSACTIONS on Information Vol.E86-D No.5 pp.956-963

Publication Date: 2003/05/01

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Type of Manuscript: PAPER

Category: Image Processing, Image Pattern Recognition

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Kiminori SATO, Nan HE, Yukitoshi TAKAHASHI, "Human Face Extraction and Recognition Using Radial Basis Function Networks" in IEICE TRANSACTIONS on Information, vol. E86-D, no. 5, pp. 956-963, May 2003, doi: .
Abstract: Partial face images, e.g., eyes, nose, and ear images are significant for face recognition. In this paper, we present a method for partial face extraction and recognition based on Radial Basis Function (RBF) networks. Focus has been centered on using ear images because they are not influenced by facial expression, and the influences of aging are negligible. Original human side face image with 320240 pixels is input, and then the RBF network locates the ear and extracts it with a 200120 pixel image. Next, another RBF network is constructed for the purpose of recognition. An algorithm that determines the radius of an RBF function is proposed. Dynamic radius, so called as compared to static one, is found through the algorithm that makes RBF functions adaptable to the training samples. We built a database that contains 600 side face images, from 100 people, to test the method and the results of both extraction and recognition are satisfied.
URL: https://global.ieice.org/en_transactions/information/10.1587/e86-d_5_956/_p

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@ARTICLE{e86-d_5_956,
author={Kiminori SATO, Nan HE, Yukitoshi TAKAHASHI, },
journal={IEICE TRANSACTIONS on Information},
title={Human Face Extraction and Recognition Using Radial Basis Function Networks},
year={2003},
volume={E86-D},
number={5},
pages={956-963},
abstract={Partial face images, e.g., eyes, nose, and ear images are significant for face recognition. In this paper, we present a method for partial face extraction and recognition based on Radial Basis Function (RBF) networks. Focus has been centered on using ear images because they are not influenced by facial expression, and the influences of aging are negligible. Original human side face image with 320240 pixels is input, and then the RBF network locates the ear and extracts it with a 200120 pixel image. Next, another RBF network is constructed for the purpose of recognition. An algorithm that determines the radius of an RBF function is proposed. Dynamic radius, so called as compared to static one, is found through the algorithm that makes RBF functions adaptable to the training samples. We built a database that contains 600 side face images, from 100 people, to test the method and the results of both extraction and recognition are satisfied.},
keywords={},
doi={},
ISSN={},
month={May},}

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TY - JOUR
TI - Human Face Extraction and Recognition Using Radial Basis Function Networks
T2 - IEICE TRANSACTIONS on Information
SP - 956
EP - 963
AU - Kiminori SATO
AU - Nan HE
AU - Yukitoshi TAKAHASHI
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E86-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2003
AB - Partial face images, e.g., eyes, nose, and ear images are significant for face recognition. In this paper, we present a method for partial face extraction and recognition based on Radial Basis Function (RBF) networks. Focus has been centered on using ear images because they are not influenced by facial expression, and the influences of aging are negligible. Original human side face image with 320240 pixels is input, and then the RBF network locates the ear and extracts it with a 200120 pixel image. Next, another RBF network is constructed for the purpose of recognition. An algorithm that determines the radius of an RBF function is proposed. Dynamic radius, so called as compared to static one, is found through the algorithm that makes RBF functions adaptable to the training samples. We built a database that contains 600 side face images, from 100 people, to test the method and the results of both extraction and recognition are satisfied.
ER -