Traditional biometric systems based on the fingerprint characteristics acquire the biometric samples using touch-based sensors. Some recent researches are focused on the design of touch-less fingerprint recognition systems based on CCD cameras. Most of these systems compute three-dimensional fingertip models and then apply unwrapping techniques in order to obtain images compatible with biometric methods designed for images captured by touch-based sensors. Unwrapped images can present different problems with respect to the traditional fingerprint images. The most important of them is the presence of deformations of the ridge pattern caused by spikes or badly reconstructed regions in the corresponding three-dimensional models. In this paper, we present a neural-based approach for the quality estimation of images obtained from the unwrapping of three-dimensional fingertip models. The paper also presents different sets of features that can be used to evaluate the quality of fingerprint images. Experimental results show that the proposed quality estimation method has an adequate accuracy for the quality classification. The performances of the proposed method are also evaluated in a complete biometric system and compared with the ones obtained by a well-known algorithm in the literature, obtaining satisfactory results.

Quality measurement of unwrapped three-dimensional fingerprints : a neural networks approach / R. Donida Labati, A. Genovese, V. Piuri, F. Scotti (PROCEEDINGS OF ... INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS). - In: Neural Networks (IJCNN)Piscataway : Institute of Electrical and Electronics Engineers (IEEE), 2012. - ISBN 9781467314886. - pp. 1-8 (( convegno International Joint Conference on Neural Networks (IJCNN) tenutosi a Brisbane nel 2012 [10.1109/IJCNN.2012.6252519].

Quality measurement of unwrapped three-dimensional fingerprints : a neural networks approach

R. Donida Labati;A. Genovese;V. Piuri;F. Scotti
2012

Abstract

Traditional biometric systems based on the fingerprint characteristics acquire the biometric samples using touch-based sensors. Some recent researches are focused on the design of touch-less fingerprint recognition systems based on CCD cameras. Most of these systems compute three-dimensional fingertip models and then apply unwrapping techniques in order to obtain images compatible with biometric methods designed for images captured by touch-based sensors. Unwrapped images can present different problems with respect to the traditional fingerprint images. The most important of them is the presence of deformations of the ridge pattern caused by spikes or badly reconstructed regions in the corresponding three-dimensional models. In this paper, we present a neural-based approach for the quality estimation of images obtained from the unwrapping of three-dimensional fingertip models. The paper also presents different sets of features that can be used to evaluate the quality of fingerprint images. Experimental results show that the proposed quality estimation method has an adequate accuracy for the quality classification. The performances of the proposed method are also evaluated in a complete biometric system and compared with the ones obtained by a well-known algorithm in the literature, obtaining satisfactory results.
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Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni
Institute of Electrical and Electronic Engineers (IEEE)
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/198276
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