Soft biometric techniques can perform a fast and unobtrusive identification within a limited number of users, be used as a preliminary screening filter, or combined in order to increase the recognition accuracy of biometric systems. The weight is a soft biometric trait which offers a good compromise between distinctiveness and permanence, and is frequently used in forensic applications. However, traditional weight measurement techniques are time-consuming and have a low user acceptability. In this paper, we propose a method for a contactless, low-cost, unobtrusive, and unconstrained weight estimation from frame sequences representing a walking person. The method uses image processing techniques to extract a set of features from a pair of frame sequences captured by two cameras. Then, the features are processed using a computational intelligence approach, in order to learn the relations between the extracted characteristics and the weight of the person. We tested the proposed method using frame sequences describing eight different walking directions, and captured in uncontrolled light conditions. The obtained results show that the proposed method is feasible and can achieve a view-independent weight estimation, also without the need of computing a complex model of the body parts.

Weight estimation from frame sequences using computational intelligence techniques / R. Donida Labati, A. Genovese, V. Piuri, F. Scotti - In: International Conference on Computational Intelligence for Measurement Systems and ApplicationsPiscataway : Institute of Electrical and Electronics Engineers (IEEE), 2012. - ISBN 9781457717772. - pp. 29-34 (( convegno CIMSA tenutosi a Tianjin nel 2012 [10.1109/CIMSA.2012.6269603].

Weight estimation from frame sequences using computational intelligence techniques

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

Abstract

Soft biometric techniques can perform a fast and unobtrusive identification within a limited number of users, be used as a preliminary screening filter, or combined in order to increase the recognition accuracy of biometric systems. The weight is a soft biometric trait which offers a good compromise between distinctiveness and permanence, and is frequently used in forensic applications. However, traditional weight measurement techniques are time-consuming and have a low user acceptability. In this paper, we propose a method for a contactless, low-cost, unobtrusive, and unconstrained weight estimation from frame sequences representing a walking person. The method uses image processing techniques to extract a set of features from a pair of frame sequences captured by two cameras. Then, the features are processed using a computational intelligence approach, in order to learn the relations between the extracted characteristics and the weight of the person. We tested the proposed method using frame sequences describing eight different walking directions, and captured in uncontrolled light conditions. The obtained results show that the proposed method is feasible and can achieve a view-independent weight estimation, also without the need of computing a complex model of the body parts.
Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni
Settore INF/01 - Informatica
Institute of Electrical and Electronics Engineers (IEEE)
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/197138
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