Bimodal Biometric Person Authentication System Using Speech and Signature Features

International Journal of Biometric and Bioinformatics 01/2010;
Source: DOAJ


Biometrics offers greater security and convenience than traditional methods of person authentication. Multi biometrics has recently emerged as a means of more robust and efficient person authentication scheme. Exploiting information from multiple biometric features improves the performance and also robustness of person authentication. The objective of this paper is to develop a robust bimodal biometric person authentication system using speech and signature biometric features. Speaker based unimodal system is developed by extracting Mel Frequency Cepstral Coefficients (MFCC) and Wavelet Octave Coefficients of Residues (WOCOR) as feature vectors. The MFCCs and WOCORs from the training data are modeled using Vector Quantization (VQ) and Gaussian Mixture Modeling (GMM) techniques. Signature based unimodal system is developed by using Vertical Projection Profile (VPP), Horizontal Projection Profile (HPP) and Discrete Cosine Transform (DCT) as features. A bimodal biometric person authentication system is then built using these two unimodal systems. Experimental results show that the bimodal person authentication system provides higher performance compared with the unimodal systems. The bimodal system is finally evaluated for its robustness using the noisy data and also data collected from the real environments. The robustness of the bimodal system is more compared to the unimodal person authentication systems.

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    • "At the fusion level, the sum Max and product rule were used in fusing the two traits together and the result obtained shows a significant improvement of bimodal biometrics matching when compared with that of direct matching score. A robust bimodal biometric authentication system based on speech and signature biometric traits was developed [9]. "
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    • "However, when these systems are applied to real-world applications, their performance can be affected by numerous factors such as noisy sensor data due to dust or lighting conditions and spoofing. Multibiometric systems that fuse multiple biometric modalities have been shown to be more robust, able to counter many of the aforementioned limitations, and are also capable of achieving higher recognition accuracies (Jain, Nandakumar, Ross 2005; Ross 2007; Eshwarappa and Latte 2010). "
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