In this paper, we describe techniques for barehanded interaction between human and computer. Barehanded means that no device and no wires are attached to the user, who controls the computer directly with the movements of his/her hand.Our approach is centered on the needs of the user. We therefore define requirements for real-time barehanded interaction, derived from application scenarios and usability considerations. Based on those requirements a finger-finding and hand-posture recognition algorithm is developed and evaluated.To demonstrate the strength of the algorithm, we build three sample applications. Finger tracking and hand posture recognition are used to paint virtually onto the wall, to control a presentation with hand postures, and to move virtual items on the wall during a brainstorming session. We conclude the paper with user tests, which were conducted to prove the usability of bare-hand human computer interaction.

This paper describes a system which uses multiple visual processes to detect

This paper describes an active-camera real-time system for tracking, shape description, and classification of the human face and mouth using only an SGI Indy computel:The system is based on use of 2 -0 blob features, which are spatially-compact clusters of pixels that are similar in terms of low-level image properties. Patterns of behavior (e.g., facial expressions and head movements) can be classified in real-time using Hidden Markov Model (HMM) methods. The system has been tested on hundreds of users and has demonstrated extremely reliable and accurate pe flormance. Typical class$cation accuracies are near 100%.

No abstract

This paper describes an active-camera real-time system for tracking, shape description, and classi"cation of the human face and mouth expressions using only a PC or equivalent computer. The system is based on use of 2-D blob features, which are spatially compact clusters of pixels that are similar in terms of low-level image properties. Patterns of behavior (e.g., facial expressions and head movements) can be classi"ed in real-time using hidden Markov models (HMMs). The system has been tested on hundreds of users and has demonstrated extremely reliable and accurate performance. Typical facial expression classi"cation accuracies are near 100%.