Abstract. Person re-identification consists in recognizing an individual that has already been observed over a network of cameras. It is a novel and challenging research topic in computer vision, for which no reference framework exists yet. Despite this, previous works share similar representations of human body based on part decomposition and the implicit concept of multiple instances. Building on these similarities, we propose a Multiple Component Matching (MCM) framework for the person reidentification problem, which is inspired by Multiple Component Learning, a framework recently proposed for object detection [3]. We show that previous techniques for person re-identification can be considered particular implementations of our MCM framework. We then present a novel person re-identification technique as a direct, simple implementation of our framework, focused in particular on robustness to varying lighting conditions, and show that it can attain state of the art performances.

Person re-identification consists of recognizing individuals across different sensors of a camera network. Whereas clothing appearance cues are widely used, other modalities could be exploited as additional information sources, like anthropometric measures and gait. In this work we investigate whether the re-identification accuracy of clothing appearance descriptors can be improved by fusing them with anthropometric measures extracted from depth data, using RGB-D sensors, in unconstrained settings. We also propose a dissimilarity-based framework for building and fusing multi-modal descriptors of pedestrian images for re-identification tasks, as an alternative to the widely used score-level fusion. The experimental evaluation is carried out on two data sets including RGB-D data, one of which is a novel, publicly available data set that we acquired using Kinect sensors. The fusion with anthropometric measures increases the first-rank recognition rate of clothing appearance descriptors up to 20%, whereas our fusion approach reduces the processing cost of the matching phase.

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The vulnerability of biometric systems to external attacks using a physical artefact in order to impersonate the legitimate user has become a major concern over the last decade. Such a threat, commonly known as 'spoofing', poses a serious risk to the integrity of biometric systems. The usual low-complexity and low-cost characteristics of these attacks make them accessible to the general public, rendering each user a potential intruder. The present study addresses the spoofing issue analysing the feasibility to perform low-cost attacks with self-manufactured three-dimensional (3D) printed models to 2.5D and 3D face recognition systems. A new database with 2D, 2.5D and 3D real and fake data from 26 subjects was acquired for the experiments. Results showed the high vulnerability of the three tested systems, including a commercial solution, to the attacks.