MARCIALIS G. L.

The decrease of representativeness of available templates during time is due to the large intra-class variations characterizing biometrics (e.g. faces). This requires the design of algorithms able to make biometric verification systems adaptive to such variations. Among others, the template co-update algo-rithm, which uses the mutual help of...

Performances of biometric recognition systems can degrade quickly when the input biometric traits exhibit substantial variations compared to the templates collected during the enrolment stage of users. This issue can be addressed using template update methods. In this paper, a novel template update method based on the concept of biometric...

The diffusion of fingerprint verification systems for security applications makes it urgent to investigate the embedding of software-based presentation attack detection algorithms (PAD) into such systems. Companies and institutions need to know whether such integration would make the system more "secure" and whether the technology available is...

Current methods for automatic template update are aimed at capturing large intra-class variations of input data and at the same time restricting the probability of impostor's introduction in client's galleries. These automatic methods avoid the costs of supervised update methods, which are due to repeated enrollment sessions and manual...

We present a novel descriptor for crowd behavior analysis and anomaly detection. The goal is to measure by appropriate patterns the speed of formation and disintegration of groups in the crowd. This descriptor is inspired by the concept of one-dimensional local binary patterns: in our case, such patterns depend on the number of group observed...

Using mobile devices, people leave latent fingerprints on the screen that can be photographed and used as a mold to fabricate high-quality spoofs. In this paper, we analysed the threat level of realistic attacks using snapshot pictures of latent fingerprints against presentation attack detection systems. Our evaluation compares a...

We present here the main research topics and activities on the design, security, safety, and robustness of machine learning models developed at the Pattern Recognition and Applications Laboratory (PRALab) of the University of Cagliari. Our findings have significantly contributed to identifying and characterizing the vulnerability of such models...

We investigated the threat level of realistic attacks using latent fingerprints against sensors equipped with state-of-art liveness detectors and fingerprint verification systems which integrate such liveness algorithms. To the best of our knowledge, only a previous investigation was done with spoofs from latent prints. In this paper, we focus...

3D face reconstruction algorithms from images and videos are applied to many fields, from plastic surgery to the entertainment sector, thanks to their advantageous features. However, when looking at forensic applications, 3D face reconstruction must observe strict requirements that still make its possible role in bringing evidence to a lawsuit...

Deepfake refers to using artificial intelligence (AI) and machine learning techniques to create compelling and realistic media content, such as videos, images, or recordings, that appear real but are fake. The most common form of deepfake involves using deep neural networks to replace or superimpose faces in existing videos or images on top of...

The advantages of deepfakes in many applications are counterbalanced by their malicious use, for example, in reply-attacks against a biometric system, identification evasion, and people harassment, when they are widespread in social networks and chatting platforms (cyberbullying) as recently documented in newspapers. Due to its "arms-race"...