Literature Survey on Multimodal Biometrics
Single model biometric systems suffer from much challenge such as noisy data, non-universality and spoof attacks. Multimodal biometric systems can resolve these limitations effectively by using two or more individual modalities. Multimodal biometric is the usage of multiple biometric indicators by personal identification systems for identifying the individuals. Multimodal authentication provides more level of authentication than unimodal biometrics which uses only one biometric data such as fingerprint or face modalities or iris. In this technique fusion of iris, Fingerprint and face traits are used in order to improve the accuracy, security of the system and to identify the human. The combination of Fingerprint, iris and face biometric can achieve performance that may not be possible using a single biometric technology. This system offer the high performance and to overcome the limitation of single modal biometrics. In this multimodal biometrics feature selection, feature extraction and feature classification these all techniques are used.
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Javier Galbally, Sebastien Marcel and Julian Fierrez (2014), “Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint and Face Recognition”, IEEE Transactions on Image Processing, Vol. 23, No. 2, February.
Hiew Moi Sim, Hishammuddin Asmuni, Rohayanti Hassan, Razib M. Othman (2014), “Multimodal Biometrics: Weighted Score Level Fusion Based on Non-Ideal Iris and Face Images”, In Science Direct, Vol. 41, 5390–5404
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Qing Zhang, Yilong Yin, De-Chuan Zhan, and Jingliang Peng (2014), “A Novel Serial Multimodal Biometrics Framework Based on Semisupervised Learning Techniques”, IEEE Transactions on Information Forensics and Security, Vol. 9, No. 10, October
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