Abstract
Multimodal biometric authentication, either verification or identification, systems are known to offer relatively better security solutions than single modality systems. In this paper, we present an identification system based on the fusion of face and hand geometry at the feature level. For face images, we use two-dimensional Discrete Cosine Transform (DCT) to extract discriminant face features which are then combined with hand geometric features. The augmented feature vectors are classified using support vector machines. We compare the performance of the proposed solution with two of the popular classifiers: rule based and decision trees. We also study the impact of feature normalization and selection on the performance. The experimental work shows that the proposed system can lead to great improvement in person identification as compared to other approaches with more than 99% accuracy and very low false acceptance and false rejection rates.
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El-Alfy, ES.M., BinMakhashen, G.M. (2012). Improved Personal Identification Using Face and Hand Geometry Fusion and Support Vector Machines. In: Benlamri, R. (eds) Networked Digital Technologies. NDT 2012. Communications in Computer and Information Science, vol 294. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30567-2_21
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DOI: https://doi.org/10.1007/978-3-642-30567-2_21
Publisher Name: Springer, Berlin, Heidelberg
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