Federico Matta

Person recognition using facial video information: A state of the art

In this article we propose a detailed state of the art on person recognition using facial video information. We classify the existing approaches present in the scientific literature between those that neglect the temporal information, and those that exploit it even partially. Concerning the first category, we detail the extensions to video data of: eigenfaces, fisherfaces, active appearance models (AAMs), radial basis function neural networks (RBFNNs), elastic graph matching (EGM), hierarchical discriminative regression trees (HDRTs) and pairwise clustering methods. After that, we focus on the strategies exploiting the temporal information, in particular those analysing: the facial motion with optical flow, the evolution of facial appearance over time with hidden Markov models (HMMs) or with various probabilistic tracking and recognition approaches, and the head motion with Gaussian mixture models.

Facial gender recognition using multiple sources of visual information

In this article we present a novel multimodal gender recognition system, which successfully integrates the head and mouth motion information with facial appearance by taking advantage of a unified probabilistic framework. In fact, we develop a temporal subsystem that has an extended feature space consisting of parameters related to head and mouth motion; at the same time, we introduce a complementary spatial subsystem based on a probabilistic extension of the eigenface approach. In the end, we implement an integration step to combine the similarity scores of the two parallel subsystems, using a suitable opinion fusion (or score fusion) strategy. The experiments show that not only facial appearance but also head and mouth motion possess a potentially relevant discriminatory power, and that the integration of different sources of biometric information from video sequences is the key strategy to develop more accurate and reliable recognition systems.

Watermarking attack: BOWS contest

In this paper, we describe some attack strategies we have applied to three different grayscale watermarked images in the particular context of BOWS (Break Our Watermarking System) Contest1; we also propose a possible use of BOWS as a teaching tool for master students.

A Behavioural Approach to Person Recognition

This paper describes a new approach for identity recognition using video sequences. While most image and video recognition systems discriminate identities using physical information only, our approach exploits the behavioral information of head dynamics; in particular the displacement signals of few head features directly extracted in the image plane. Due to the lack of standard video database, identification and verification scores have been obtained using a small collection of video sequences: the results for this new approach are nevertheless promising

Video Face Recognition: A Physiological and Behavioural Multimodal Approach

In this article we present a multimodal system to person recognition by integrating two complementary approaches that work with video data. The first module exploits the behavioural information: it is based on statistical features computed using the displacement signals of a head; the second one is dealing with the physiological information: it is a probabilistic extension of the classic Eigenface approach. For a consistent fusion, both systems share the same probabilistic classification framework: a Gaussian mixture model (GMM) approximation and a Bayesian classifier. We assess the performances of the multimodal system by implementing two fusion strategies and we analyse their evolution in presence of artificial noise.

Person Recognition based on Head and Mouth Dynamics

Face is considered as an attractive biometric but because of multiple sources of variabilities, the associated recognition rate is not high enough, when working on appearance only, for most of real applications. Considering that most available visual data are videos and not still images, we investigate in this article the possible contribution of some dynamic parameters (head displacements and mouth motion) in person recognition. Some preliminary results tend to validate this original proposal that opens some new perspectives in the possible design of future hybrid and efficient system combining appearance and dynamics of faces

Towards person recognition using head dynamics

This paper describes a new approach for identity recognition using video sequences. While most image and video recognition systems discriminate identities using pixel-based information, our approach exploits the head dynamics; in particular the displacement signals of few head features. Due to the lack of standard video database, identification and verification scores have been obtained using a small collection of video sequences. The results for this preliminary approach are nevertheless encouraging.