Gesture recognition through angle space

As the notion of ubiquitous computing becomes a reality, the keyboard and mouse paradigm become less satisfactory as an input modality. The ability to interpret gestures can open another dimension in the user interface technology. In this paper, we present a novel approach for dynamic hand gesture modeling using neural networks. The results show high accuracy in detecting single and multiple gestures, which makes this a promising approach for gesture recognition from continuous input with undetermined boundaries. This method is independent of the input device and can be applied as a general back-end processor for gesture recognition systems

Recognizing human motion using eigensequences

This paper presents a novel method for motion recognition. The approach is based on 3D motion data. The captured motion is divided into sequences, which are sets of contiguous postures over time. Each sequence is then classified into one of the recognizable action classes by means of a PCA based method. The proposed approach is able to perform automatic recognition of movements containing more than one class of action. The advantages of this technique are that it can be easily extended to recognize many action classes and, most of all, that the recognition process is real-time. In order to fully understand the capabilities of the proposed method, the approach has been implemented and tested in a virtual environment. Several experimental results are also provided and discussed

Machine Analysis of Facial Expressions

No abstract