Existing person search methods mainly focus on searching for the target using database images. However, this is different from real-world surveillance videos which involve a temporal relationship between video frames. To solve problem, we propose an efficient method that employs spatio-temporal features in videos. This not only considers spatial of persons each frame, but also utilizes same adj...

Extracting effective and discriminative features is highly important for addressing the challenges of person re-identification (re-ID). At present, deep convolutional neural networks typically use high-level to identify pedestrians. However, some essential spatial information contained in low-level will be lost during learning feature. Most existing re-ID methods rely primarily on hand-crafted ...

In this paper, we introduce a cognitive approach for person tracking on a mobile platform. The approach is based on a biologically motivated attention system which is able to detect regions of interest in images based on concepts of the human visual system. A top-down guided visual search module of the system enables to especially highlight features which fit to a previously learned target obje...

Recent studies suggest that co-acting with another person induces a problem to discriminate between one's own and the other's actions which can be resolved by emphasizing action features that discriminate best between both persons' actions in a given task context. Mostly, overt action features like the spatial position of responses have been suggested as discriminating action features. In the p...

In this paper, we propose two new approaches for extracting mouth features for authenticating the person identity with liveness checks. The novel correlated audio-lip features and tensor lip-motion features allow liveness checks to be included in the person identity authentication systems, and ensures that the biometric cues are acquired from a live person who is actually present at the time of...