Abstract
Biometrics is physical or behavioral characteristics to identify a human digitally. The identification of humans involves training and testing process. In general, the training process requires a large database to store the extracted features of multiple samples of a human, and the lighting conditions of acquiring samples also make it more complex. These challenges of using single biometric can be addressed by using multimodal biometrics. In this paper, multimodal biometrics of humans like face, fingerprint, and iris are considered for their easy and secure recognition among the group of people. The testing process involves the comparison of the features of real-time sample with the features of training samples, and consequently, the recognition rate of this algorithm increases. This algorithm uses discrete wavelet transform (DWT) for the filtering and compression, singular value decomposition (SVD) for the extraction of features from the compressed and filtered results of DWT. The proposed method gives better performance like 98% of accuracy and 100% recognition rate.
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Elisha Raju, B., Ramesh Chandra, K., Budumuru, P.R. (2022). A Two-Level Security System Based on Multimodal Biometrics and Modified Fusion Technique. In: Karrupusamy, P., Balas, V.E., Shi, Y. (eds) Sustainable Communication Networks and Application. Lecture Notes on Data Engineering and Communications Technologies, vol 93. Springer, Singapore. https://doi.org/10.1007/978-981-16-6605-6_2
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DOI: https://doi.org/10.1007/978-981-16-6605-6_2
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