Real time face and mouth recognition using radial basis function neural networks
This paper presents a method for automatic real time face and mouth recognition using radial basis function neural networks (RBFNN). The proposed method uses the motion information to localize the face region, and the face region is processed in YC r C b color space to determine the locations of the...
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| Published in | Expert systems with applications Vol. 36; no. 3; pp. 6879 - 6888 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.04.2009
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0957-4174 1873-6793 |
| DOI | 10.1016/j.eswa.2008.08.001 |
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| Abstract | This paper presents a method for automatic real time face and mouth recognition using radial basis function neural networks (RBFNN). The proposed method uses the motion information to localize the face region, and the face region is processed in
YC
r
C
b
color space to determine the locations of the eyes. The center of the mouth is determined relative to the locations of the eyes. Facial and mouth features are extracted using multiscale morphological erosion and dilation operations, respectively. The facial features are extracted relative to the locations of the eyes, and mouth features are extracted relative to the locations of the eyes and mouth. The facial and mouth features are given as input to radial basis function neural networks. The RBFNN is used to recognize a person in video sequences using face and mouth modalities. The evidence from face and mouth modalities are combined using a weighting rule, and the result is used for identification and authentication. The performance of the system using facial and mouth features is evaluated in real time in the laboratory environment, and the system achieves a recognition rate (RR) of 99.0% and an equal error rate (EER) of about 0.73% for 50 subjects. The performance of the system is also evaluated for XM2VTS database, and the system achieves a recognition rate (RR) of 100% an equal error rate (EER) of about 0.25% for 50 subjects. |
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| AbstractList | This paper presents a method for automatic real time face and mouth recognition using radial basis function neural networks (RBFNN). The proposed method uses the motion information to localize the face region, and the face region is processed in YCrCb color space to determine the locations of the eyes. The center of the mouth is determined relative to the locations of the eyes. Facial and mouth features are extracted using multiscale morphological erosion and dilation operations, respectively. The facial features are extracted relative to the locations of the eyes, and mouth features are extracted relative to the locations of the eyes and mouth. The facial and mouth features are given as input to radial basis function neural networks. The RBFNN is used to recognize a person in video sequences using face and mouth modalities. The evidence from face and mouth modalities are combined using a weighting rule, and the result is used for identification and authentication. The performance of the system using facial and mouth features is evaluated in real time in the laboratory environment, and the system achieves a recognition rate (RR) of 99.0% and an equal error rate (EER) of about 0.73% for 50 subjects. The performance of the system is also evaluated for XM2VTS database, and the system achieves a recognition rate (RR) of 100% an equal error rate (EER) of about 0.25% for 50 subjects. This paper presents a method for automatic real time face and mouth recognition using radial basis function neural networks (RBFNN). The proposed method uses the motion information to localize the face region, and the face region is processed in YC r C b color space to determine the locations of the eyes. The center of the mouth is determined relative to the locations of the eyes. Facial and mouth features are extracted using multiscale morphological erosion and dilation operations, respectively. The facial features are extracted relative to the locations of the eyes, and mouth features are extracted relative to the locations of the eyes and mouth. The facial and mouth features are given as input to radial basis function neural networks. The RBFNN is used to recognize a person in video sequences using face and mouth modalities. The evidence from face and mouth modalities are combined using a weighting rule, and the result is used for identification and authentication. The performance of the system using facial and mouth features is evaluated in real time in the laboratory environment, and the system achieves a recognition rate (RR) of 99.0% and an equal error rate (EER) of about 0.73% for 50 subjects. The performance of the system is also evaluated for XM2VTS database, and the system achieves a recognition rate (RR) of 100% an equal error rate (EER) of about 0.25% for 50 subjects. |
| Author | Palanivel, S. Ramalingam, V. Balasubramanian, M. |
| Author_xml | – sequence: 1 givenname: M. surname: Balasubramanian fullname: Balasubramanian, M. email: balu_june1@yahoo.co.in – sequence: 2 givenname: S. surname: Palanivel fullname: Palanivel, S. email: spal_yughu@yahoo.com – sequence: 3 givenname: V. surname: Ramalingam fullname: Ramalingam, V. email: aucsevr@yahoo.com |
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| Keywords | Face tracking Eye location Multiscale morphological dilation and erosion operations Radial basis function neural network |
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| SubjectTerms | Eye location Face tracking Multiscale morphological dilation and erosion operations Radial basis function neural network |
| Title | Real time face and mouth recognition using radial basis function neural networks |
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