Abstract
The paper addresses the unimodal and multimodal (fusion prior to matching) biometric recognition system from the promising traits face and iris which uniquely identify humans. Performance measures such as precision, recall, and f-measure and also the training time in building up the compact model, prediction speed of the observations are tabulated which gives the comparison between unimodal and multimodal biometric recognition system. LPQ features are extracted for both the modalities and LDA is employed for dimensionality reduction, KNN (linear and weighted), and SVM (linear and nonlinear) classifiers are adopted for classification. Our empirical evaluation shows our proposed method is potential with 99.13% of recognition accuracy under feature level fusion and computationally efficient.
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Supreetha Gowda, H.D., Hemantha Kumar, G., Imran, M. (2019). Multimodal Biometric Recognition System Based on Nonparametric Classifiers. In: Nagabhushan, P., Guru, D., Shekar, B., Kumar, Y. (eds) Data Analytics and Learning. Lecture Notes in Networks and Systems, vol 43. Springer, Singapore. https://doi.org/10.1007/978-981-13-2514-4_23
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DOI: https://doi.org/10.1007/978-981-13-2514-4_23
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