A. Genovese, V. Piuri, F. Scotti, Towards explainable face aging with Generative Adversarial Networks, Proc. of the 26th IEEE Int. Conf. on Image Processing (ICIP 2019), pp. 3806-3810, Taipei, Taiwan, September 2019, ISSN 978-1-5386-6249-6.
Biometric systems
Soft Biometrics
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.
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.
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.
Relevant publications
A. Anand, R. Donida Labati, A. Genovese, E. Muñoz, V. Piuri, F. Scotti, Age estimation based on face images and pre-trained Convolutional Neural Networks, Proc. of the 2017 IEEE Symp. on Computational Intelligence for Security and Defense Applications (CISDA 2017), pp. 1-7, Honolulu, HI, USA, November 2017, ISSN 978-1-5386-2726-6.
A. Anand, R. Donida Labati, M. Hanmandlu, V. Piuri, F. Scotti, Text-independent speaker recognition for ambient intelligence applications by using information set features, Proc. of the 2017 IEEE Int. Conf. on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA 2017), pp. 30-35, Annecy, France, July 2017, ISSN 978-1-5090-4253-1.
Y. Zhai, J. Liu, J. Zeng, V. Piuri, F. Scotti, Z. Ying, Y. Xu, J. Gan, Deep Convolutional Neural Network for facial expression recognition, Proc. of the Int. Conf. on Image and Graphics (ICIG 2017), pp. 211-223, Springer, 2017, ISSN 978-3-319-71607-7.
R. Donida Labati, A. Genovese, V. Piuri, F. Scotti, Weight estimation from frame sequences using computational intelligence techniques, Proc. of the 2012 IEEE Int. Conf. on Computational Intelligence for Measurement Systems and Applications (CIMSA 2012), pp. 29-34, Tianjin, China, July 2012, ISSN 978-1-4577-1777-2.