원문정보
보안공학연구지원센터(IJMUE)
International Journal of Multimedia and Ubiquitous Engineering
Vol.10 No.10
2015.10
pp.75-82
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
In this article, a multi-tone signal generated by micro-speaker is adopted as the acoustic stimulation, and two microphones are used to collect the DPOAE data from human ear and background noise respectively. Otoacoustic emission is modeled based on Volterra kernel. The feature of human ear’s DPOAE feature model is extracted intelligently by improved stimulation annealing genetic mixed algorithm. In order to apply this model feature to identification, its feasibility is verified by BP neural network. This provides a new biometric method for identity authentication.
목차
Abstract
1. Introduction
2. DPOAE and its Detection
3. DPOAE Signal Acquisition Sensor and its Measurement Channel
4. POAE Characteristic Model Based on Volterra Kernel
4.1. The Extraction Method of Volterra Kernel
4.2. Experimental Data Acquisition
4.3. DPOAE Feature Modeling of Ear
4.4. Based on Intelligent Feature Extraction and BP Neural Network of Biometric Identification Experiments and Results
5. Conclusions
Acknowledgements
References
1. Introduction
2. DPOAE and its Detection
3. DPOAE Signal Acquisition Sensor and its Measurement Channel
4. POAE Characteristic Model Based on Volterra Kernel
4.1. The Extraction Method of Volterra Kernel
4.2. Experimental Data Acquisition
4.3. DPOAE Feature Modeling of Ear
4.4. Based on Intelligent Feature Extraction and BP Neural Network of Biometric Identification Experiments and Results
5. Conclusions
Acknowledgements
References
저자정보
참고문헌
자료제공 : 네이버학술정보