Gustavo Führ

Pietro Melzi, Ruben Tolosana, Ruben Vera-Rodriguez et al.

Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must be covered in more detail. This paper offers an overview of the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) organized at WACV 2024. This is the first international challenge aiming to explore the use of synthetic data in face recognition to address existing limitations in the technology. Specifically, the FRCSyn Challenge targets concerns related to data privacy issues, demographic biases, generalization to unseen scenarios, and performance limitations in challenging scenarios, including significant age disparities between enrollment and testing, pose variations, and occlusions. The results achieved in the FRCSyn Challenge, together with the proposed benchmark, contribute significantly to the application of synthetic data to improve face recognition technology.

Gustavo Fuhr, Claudio Rosito Jung

This paper presents a novel hierarchical approach for collective behavior recognition based solely on ground-plane trajectories. In the first layer of our classifier, we introduce a novel feature called Personal Interaction Descriptor (PID), which combines the spatial distribution of a pair of pedestrians within a temporal window with a pyramidal representation of the relative speed to detect pairwise interactions. These interactions are then combined with higher level features related to the mean speed and shape formed by the pedestrians in the scene, generating a Collective Behavior Descriptor (CBD) that is used to identify collective behaviors in a second stage. In both layers, Random Forests were used as classifiers, since they allow features of different natures to be combined seamlessly. Our experimental results indicate that the proposed method achieves results on par with state of the art techniques with a better balance of class errors. Moreover, we show that our method can generalize well across different camera setups through cross-dataset experiments.