Human activity recognition has been an important area of computer vision research since
the 1980s. Various approaches have been proposed with a great portion of them addressing …

Human action recognition based on skeletons has wide applications in human–computer
interaction and intelligent surveillance. However, view variations and noisy data bring …

Three-dimensional hand gesture recognition has attracted increasing research interests in
computer vision, pattern recognition, and human-computer interaction. The emerging depth …

Abstract Continuous Sign Language Translation (SLT) is a challenging task due to its
specific linguistics under sequential gesture variation without word alignment. Current hybrid …

Local spatio-temporal interest points (STIPs) and the resulting features from RGB videos
have been proven successful at activity recognition that can handle cluttered backgrounds …

Despite important recent advances, the vulnerability of biometric systems to spoofing attacks
is still an open problem. Spoof attacks occur when impostor users present synthetic …

This paper presents a novel multi-cue framework for scene segmentation, involving a
combination of appearance (grayscale images) and depth cues (dense stereo vision). An …

For one-shot learning gesture recognition, two important challenges are: how to extract
distinctive features and how to learn a discriminative model from only one training sample …

This work addresses the problem of human body segmentation from multi-modal visual cues
as a first stage of automatic human behavior analysis. We propose a novel RGB–depth …

In this paper, we propose using 3D Convolutional Neural Networks for large scale user-
independent continuous gesture recognition. We have trained an end-to-end deep network …