Facial Behavior Analysis using 4D Curvature Statistics for Presentation Attack Detection

The uniqueness, complexity, and diversity of facial shapes and expressions led to success of facial biometric systems. Regardless of the accuracy of current facial recognition methods, most of them are vulnerable against the presentation of sophisticated masks. In the highly monitored application scenario at airports and banks, fraudsters probably do not wear masks. However, a deception will become more probable due to the increase of unsupervised authentication using kiosks, eGates and mobile phones in self-service. To robustly detect elastic 3D masks, one of the ultimate goals is to automatically analyze the plausibility of the facial behavior based on a sequence of 3D face scans. Most importantly, such a method would also detect all less advanced presentation attacks using static 3D masks, bent photographs with eyeholes, and replay attacks using monitors. Our proposed method achieves this goal by comparing the temporal curvature change between presentation attacks and genuine faces. For evaluation purposes, we recorded a challenging database containing replay attacks, static and elastic 3D masks using a high-quality 3D sensor. Based on the proposed representation, we found a clear separation between the low facial expressiveness of presentation attacks and the plausible behavior of genuine faces.

Causal Inference in Nonverbal Dyadic Communication with Relevant Interval Selection and Granger Causality

Human nonverbal emotional communication in dyadic dialogs is a process of mutual influence and adaptation. Identifying the direction of influence, or cause-effect relation between participants is a challenging task, due to two main obstacles. First, distinct emotions might not be clearly visible. Second, participants cause-effect relation is transient and variant over time. In this paper, we address these difficulties by using facial expressions that can be present even when strong distinct facial emotions are not visible. We also propose to apply a relevant interval selection approach prior to causal inference to identify those transient intervals where adaptation process occurs. To identify the direction of influence, we apply the concept of Granger causality to the time series of facial expressions on the set of relevant intervals. We tested our approach on synthetic data and then applied it to newly, experimentally obtained data. Here, we were able to show that a more sensitive facial expression detection algorithm and a relevant interval detection approach is most promising to reveal the cause-effect pattern for dyadic communication in various instructed interaction conditions.