Multimodal priority verification of face and speech using momentum back-propagation neural network
- Authors
- Park, Changhan; Ki, Myungseok; Namkung, Jaechan; Paik, Joonki
- Issue Date
- Jun-2006
- Publisher
- SPRINGER-VERLAG BERLIN
- Citation
- ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 2, PROCEEDINGS, v.3972, pp 140 - 149
- Pages
- 10
- Journal Title
- ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 2, PROCEEDINGS
- Volume
- 3972
- Start Page
- 140
- End Page
- 149
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/40229
- DOI
- 10.1007/11760023_22
- ISSN
- 0302-9743
1611-3349
- Abstract
- In this paper, we propose a priority verification method for multimodal biometric features by using a momentum back-propagation artificial neural network (MBP-ANN). We also propose a personal verification method using both face and speech to improve the rate of single biometric verification. False acceptance rate (FAR) and false rejection rate (FRR) have been a fundamental bottleneck of real-time personal verification. The proposed multimodal biometric method is to improve both verification rate and reliability in real-time by overcoming technical limitations of single biometric verification methods. The proposed method uses principal component analysis (PCA) for face recognition and hidden markov model (HMM) for speech recognition. It also uses MBP-ANN for the final decision of personal verification. Based on experimental results, the proposed system can reduce FAR down to 0.0001%, which proves that the proposed method overcomes the limitation of single biometric system and proves stable personal verification in real-time.
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- Appears in
Collections - Graduate School of Advanced Imaging Sciences, Multimedia and Film > Department of Imaging Science and Arts > 1. Journal Articles
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