Projects

COSMOS

The ever-growing number of travelers and migrants crossing the EU borders poses a serious challenge to the border control authorities in terms of a reduced amount of time for carrying out border checks. Consequently, efforts are being undertaken to facilitate the travel of bona-fide and genuine passengers and, at the same time, to safeguard a high level of security. In this kind of context, the use of multimodal biometrics might provide the key for increasing the level of security while reducing the failures inevitably associated with the use of a single identifier in a typically uncontrolled environment. As a further element to consider, practical experiences lead to privilege a most fluent and non-intrusive control process for non-critical travelers (EU, bona-fide etc.). Therefore the use of contactless capturing techniques and their implementation on consumer-level mobile devices is likely to be preferred over contact-based technologies and dedicated devices. Overall, COSMOS (COntactlesS Multibiometric mObile System in the wild) aims at delivering a comprehensive approach to multi-biometric person verification and recognition, including most contact-less biometrics, flexibly integrated through a context-adaptive acquisition/matching strategy based on their complementarity and exploiting the agile and ubiquitous hardware platforms represented by last generation smartphones and tablets. More in practice, the project will exploit the specific knowledge of each of the participants to provide an unprecedented unified biometric platform for contactless person verification/recognition by means of both hard biometrics like face (both in 2D and 3D), iris, ear, fingerprint/palmprint and soft biometrics like gait and gaze. Moreover, multi-tracking methods will be also developed to enabling screening-from-distance capabilities to allow the proposed system to detect subjects of interest or potential threats to be checked in detail by the other biometric modalities. COSMOS is expected to fostering the research and application of new ideas in the field of biometry by providing three major contributions to the field: the effective and efficient implementation of the single modalities on mobile architectures in the challenging "in-the-wild" scenario; the complimentary integration of these biometrics by innovative data fusion strategies to maximize the discriminating potential of the different identifiers considered according to a wide range of operative conditions and the novel smart management of the crucial privacy issues related to a multi-biometric system. Finally, since in Italy as well in other EU and extra-EU countries, strict data protection prescriptions regulate the use of biometrics, COSMOS will devote a specific emphasis to data protection, social, medical and ethical issues.

COntactlesS Multibiometric mObile System in the wild


Software

Source code of FusionNet for touchless palmprint and finger texture recognition
https://github.com/AngeloUNIMI/Demo_FusionNet
http://iebil.di.unimi.it/fusionnet/index.htm

FusionNet for touchless palmprint and finger texture recognition

References
  • A. Genovese, V. Piuri, F. Scotti, S. Vishwakarma, "Touchless palmprint and finger texture recognition: A Deep Learning fusion approach", in Proc. of the 2019 IEEE Int. Conf. on Computational Intelligence & Virtual Environments for Measurement Systems and Applications (CIVEMSA 2019), Tianjin, China, pp. 1-6, June 14-16, 2019. ISBN: 978-1-5386-8344-6. [DOI: 10.1109/CIVEMSA45640.2019.9071620][PDF]
  • R. Donida Labati, E. Muñoz, V. Piuri, A. Ross, F. Scotti, "Non-ideal iris segmentation using Polar Spline RANSAC and illumination compensation", in Computer Vision and Image Understanding, Elsevier, November, 2019. ISSN: 1077-3142. [DOI: 10.1016/j.cviu.2019.07.007][PDF]
  • A. Genovese, V. Piuri, F. Scotti, "Towards explainable face aging with Generative Adversarial Networks", in Proc. of the 26th IEEE Int. Conf. on Image Processing (ICIP 2019), Taipei, Taiwan, pp. 3806-3810, September 22-25, 2019. ISBN: 978-1-5386-6249-6. [DOI: 10.1109/ICIP.2019.8803616][PDF]
  • R. Donida Labati, E. Muñoz, V. Piuri, R. Sassi, F. Scotti, "Deep-ECG: Convolutional Neural Networks for ECG biometric recognition", in Pattern Recognition Letters, Elsevier, pp. 78-85, September, 2019. ISSN: 0167-8655. [DOI: 10.1016/j.patrec.2018.03.028][PDF]
  • R. Donida Labati, A. Genovese, V. Piuri, F. Scotti, "A scheme for fingerphoto recognition in smartphones", in Selfie Biometrics, A. Rattani, R. Derakhshani, A. Ross (eds.), Springer, Cham, pp. 49-66, 2019. ISBN: 978-3-030-26972-2. [DOI: 10.1007/978-3-030-26972-2_3][PDF]
  • A. Genovese, V. Piuri, K. N. Plataniotis, F. Scotti, "PalmNet: Gabor-PCA Convolutional Networks for touchless palmprint recognition", in IEEE Transactions on Information Forensics and Security, pp. 3160-3174, December, 2019. ISSN: 1556-6013. [DOI: 10.1109/TIFS.2019.2911165][PDF]
  • A. Genovese, E. Muñoz, V. Piuri, F. Scotti, "Advanced biometric technologies: emerging scenarios and research trends", in From Database to Cyber Security: Essays Dedicated to Sushil Jajodia on the Occasion of His 70th Birthday, P. Samarati, I. Ray, I. Ray (eds.), Springer International Publishing, Cham, pp. 324-352, 2018. ISBN: 978-3-030-04834-1. [DOI: 10.1007/978-3-030-04834-1_17][PDF]
  • R. Donida Labati, A. Genovese, E. Muñoz, V. Piuri, F. Scotti, "A novel pore extraction method for heterogeneous fingerprint images using Convolutional Neural Networks", in Pattern Recognition Letters, pp. 58-66, October, 2018. ISSN: 0167-8655. [DOI: 10.1016/j.patrec.2017.04.001][PDF]
  • 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", in Proc. of the 2017 IEEE Symp. on Computational Intelligence for Security and Defense Applications (CISDA 2017), Honolulu, HI, USA, pp. 1-7, November 27-30, 2017. ISBN: 978-1-5386-2726-6. [DOI: 10.1109/SSCI.2017.8285381][PDF]
  • A. Anand, R. Donida Labati, M. Hanmandlu, V. Piuri, F. Scotti, "Text-independent speaker recognition for ambient intelligence applications by using information set features", in Proc. of the 2017 IEEE Int. Conf. on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA 2017), Annecy, France, pp. 30-35, July 26-28, 2017. ISBN: 978-1-5090-4253-1. [DOI: 10.1109/CIVEMSA.2017.7995297]