Massimo Tistarelli

Massimo Tistarelli received the Phd in Computer Science and Robotics in 1991 from the University of Genoa. He is Full Professor in Computer Science (with tenure) and director of the Computer Vision Laboratory at the University of Sassari, Italy. Since 1986 he has been involved as project coordinator and task manager in several projects on computer vision and biometrics funded by the European Community.

Since 1994 he has been the director of the Computer Vision Laboratory at the Department of Communication, Computer and Systems Science of the University of Genoa, and now at the University of Sassari, leading several National and European projects on computer vision applications and image-based biometrics.

Prof. Tistarelli is a founding member of the Biosecure Foundation, which includes all major European research centers working in biometrics. His main research interests cover biological and artificial vision (particularly in the area of recognition, three-dimensional reconstruction and dynamic scene analysis), pattern recognition, biometrics, visual sensors, robotic navigation and visuo-motor coordination. He is one of the world-recognized leading researchers in the area of biometrics, especially in the field of face recognition and multimodal fusion.

He is coauthor of more than 200 scientific papers in peer reviewed books, conferences and international journals. He is the principal editor for the Springer books “Handbook of Remote Biometrics” and “Handbook of Biometrics for Forensic Science”.

Prof. Tistarelli organized and chaired several world-recognized several scientific events and conferences in the area of Computer Vision and Biometrics. He has been associate editor for several scientific journals including IEEE Transactions on TBIOM, IEEE Transactions on PAMI, IEEE Transactions on Emerging Tecnologies, IET Biometrics and Pattern Recognition Letters.

Since 2003 he is the founding director for the Int.l Summer School on Biometrics (now at the 22nd edition – https://biometrics.uniss.it). He served as vice president of the IEEE Biometrics Council, first vice president of the IAPR and chair of the IAPR Fellow committee. He is a Fellow member of the IAPR and Senior member of the IEEE. In 2022 he was awarded the IEEE Biometrics Council Meritorious Service Award.

Contact: tista@uniss.it

Abstracts

Face Recognition: a Vision Ahead Reflections on 30 years of face recognition research

Face recognition is possibly one of the most succesfull applications of Computer Vision and AI. Today's information technology allowed to deploy face recognition in several domains, ranging from automated border control to mobile device authentication.

Even though the progress in computing power and machine learning allowed to implement very fast and efficient systems, there are still several issues which remain unsolved. On the other hand, the basic "face recognition pipeline", conceived 30 years ago, still remains unaltered. As such, we need to learn from the past and address some research questions which are still unanswered. Among them:

1. If face recognition is a "solved" problem, why are we still doing research on this topic?

2. What are the drawbacks and limitations of current deep learning models? How far can we go by exploiting increasing amounts of face data?

3. Is the human visual system still the best comparative face recognition model? If so, what can we learn from the way humans recognize faces?

4. How can we build "ethical" systems which propery address current privacy concerns?

In this talk we'll address these questions, trying to envisage a path forward with the aim of driving our research curiosity towards the design of tomorrow's intelligent machines.

Human Face Recognition: Learning from Biological Deep Networks

Face Recognition has been extensively studied as a mean to facilitate man-machine interaction in a variety of different applications. Due to the imaging variabilities and to the complex nature of the face shape and dynamics, analyzing and recognizing human faces from digital images is still a very complex task.

In the last decade deep learning techniques have strongly influenced many aspects of computational vision. Many difficult vision tasks can now be performed by deploying a properly tailored and trained deep network. Oxford University’s VGG-face is possibly the first deep convolutional network designed to perform face recognition, obtaining unsurpassed performance at the time it was firstly proposed. The enthusiasm for deep learning is unfortunately paired by the present lack of a clear understanding of how they work and why they provide such brilliant performance. The same applies to Face Recognition.

Over the last years, several and more complex deep convolutional networks, trained on very large, mainly private, datasets, have been proposed still elevating the performance bar also on quite challenging public databases, such as the Janus IJB-A and IJB-B. Despite of the progress in the development of such networks, and the advance in the learning algorithms, the insight on these networks is still very limited. For this reason, in this talk we analyse the neural architecture of the early stages of the human visual system to devise a biologically-inspired model for face recognition. The aim is not pushing the recognition performance further, but to better understand the representation space produced from a deep network and how it may help explaining the process undergoing a real biological neural architecture.

In this talk we analyse an hybrid model network trying to better understand the role of the different layers, including the retino-cortical mapping simulated by a log-polar image resampling. The following issues will be addressed:

• What is the representation space within a deep convolutional network and how this reflects the organization of the human visual cortex.

• How the retino-cortical mapping, implemented in the human visual system, may impact the representation space, hence improving the classification performance.

• The relevance of peripheral vs foveal vision, coupled with visual attention, for face recognition.

Forensic Biometrics: Bridging Technology with Forensic Science

In the last decades, digital technologies have been applied in forensic investigations only to a limited extent of their possibilities. A number of factors have hindered the wider adoption of these technologies to operational scenarios. However, there has been a number of successful applications where digital biometric technologies were crucial to support investigation and to provide evidence in court. Given the great potential of biometric technologies for objective and quantitative evidence evaluation, it would be desirable to see a wider deployment of these technologies, in a standardized manner, among police forces and forensic institutes.

In this talk, the actual state of the art in forensic biometric systems will be briefly reviewed, trying to identify the outbreaks and pitfalls of current technologies. Despite of their impressive performance, some recent biometric technologies when applied to forensic evaluation demonstrated sometimes to be lacking under some respect. Other technologies will need adaptations to be ready for the forensic field. We postulate that there is a challenge to be faced with more advanced tools and testing on operational data. This will require a joint effort involving stakeholders and scientists from multiple disciplines as well as a greater involvement of forensic institutes and police forensic science departments.