Abstract
Biometric systems are nowadays gaining significant attraction due to their ability to uniquely authenticate a person on the basis of his/her unique personal body features. But, most of the deployed biometric systems still use biometric information from a single biometric trait for authentication or recognition purposes. It is well known that biometric systems have to deal with large population coverage, noisy sensor data, different deployment platforms, susceptibility to spoof attacks, and challenging recognition performance requirements. It is found difficult for unimodal biometric systems to overcome these problems. The multimodal biometric systems are able to meet these demands by integrating information from multiple biometric traits, multiple algorithms, multiple sensors, multiple instances, and multiple samples. Hence, a new multimodal biometric system is proposed in this paper which combines finger knuckle print, fingerprint, and palmprint at match-score level. The resulting match score is utilized to state whether the person is authentic or fraud. The experimental results illustrate the effectiveness of proposed multimodal biometric system pertaining to False-Accept-Rate (FAR), False-Reject-Rate (FRR), and Genuine-Accept-Rate (GAR).
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Kant, C., Chaudhary, S. (2021). A Multimodal Biometric System Based on Finger Knuckle Print, Fingerprint, and Palmprint Traits. In: Sharma, M.K., Dhaka, V.S., Perumal, T., Dey, N., Tavares, J.M.R.S. (eds) Innovations in Computational Intelligence and Computer Vision. Advances in Intelligent Systems and Computing, vol 1189. Springer, Singapore. https://doi.org/10.1007/978-981-15-6067-5_21
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