Abstract
Biometric based personal recognition has become the integral part for administrate and operational aspects for private and government sector. This paper proposed the soft feature based personal recognition by integration of unique and non-unique or soft features to improve the performance of biometrics recognition system. The proposed biometrics recognition system uses unique features of back surface of finger called finger knuckle (FK) and complementary features known as soft features such as age and gender of each individual. Further author presents combination Walsh wavelet Transform based finger knuckle features and soft features at score level using Bayes formula. The algorithm is tested on own FK database and results demonstrated that performance of primary FK recognition system has improved by integrating soft features of same traits. Error Equal Rate (EER) of integrated biometrics is 6.5% which is less as compared to primary.
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Kulkarni, S., Raut, R., Bhosle, U. (2018). Soft Feature Based Personal Recognition. In: Abraham, A., Cherukuri, A., Madureira, A., Muda, A. (eds) Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016). SoCPaR 2016. Advances in Intelligent Systems and Computing, vol 614. Springer, Cham. https://doi.org/10.1007/978-3-319-60618-7_3
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DOI: https://doi.org/10.1007/978-3-319-60618-7_3
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