Person Recognition at a Distance: Improving Face Recognition Through Body Static Information

In this paper we evaluate body static information to improve the performance of face recognition at a distance. To this aim, we assess one state-of-the-art face recognition system based on deep features and three body-based person recognition systems, namely: i) row profiles with correlation coefficient, ii) row and column profiles with Support Vector Machines, and iii) contour coordinates with Dynamic Time Warping. Results are reported using the Multi-Biometric Tunnel Database, emphasizing on three distance settings: far, medium, and close, ranging from full body exposure to head and shoulders exposure. Several conclusions can be drawn from this work: a) row and column profiles are more robust than contour coordinates, b) face-based systems perform poorly at far distances, being body-based information more reliable at that distances, c) in general face-based systems perform better than body-based approaches at medium and close distances, and d) the multimodal fusion approach manages to outperform face-only recognition at distance in all distance-settings considered.