Facial micro-expressions were proven to be an important behaviour source for hostile intent and danger demeanour detection [1]. In this paper, we present a novel approach for facial micro-expressions recognition in video sequences. First, 200 frame per second (fps) high speed camera is used to capture the face. Second, the face is divided to specific regions, then the motion in each region is recognized based on 3D-Gradients orientation histogram descriptor. For testing this approach, we create a new dataset of facial micro-expressions, that was manually tagged as a ground truth, using a high speed camera. In this work, we present recognition results of 13 different micro-expressions.

SUMMARYFacial micro-expressions are fast and subtle facial motions that are considered as one of the most useful external signs for detecting hidden emotional changes in a person. However, they are not easy to detect and measure as they appear only for a short time, with small muscle contraction in the facial areas where salient features are not available. We propose a new computer vision method for detecting and measuring timing characteristics of facial micro-expressions. The core of this method is based on a descriptor that combines pre-processing masks, histograms and concatenation of spatial-temporal gradient vectors. Presented 3D gradient histogram descriptor is able to detect and measure the timing characteristics of the fast and subtle changes of the facial skin surface. This method is specifically designed for analysis of videos recorded using a hi-speed 200 fps camera. Final classification of micro expressions is done by using a k-mean classifier and a voting procedure. The Facial Action Coding System was utilized to annotate the appearance and dynamics of the expressions in our new hi-speed micro-expressions video database. The efficiency of the proposed approach was validated using our new hi-speed video database. key words: facial motion analysis, high speed camera, video descriptor

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This paper proposes a method to realize a 3D video system that can capture video data from multiple cameras, reconstruct 3D models, transmit 3D video streams via the network, and display them on remote PCs. All processes are done in real time. We represent a player with a simplified 3D model consisting of a single plane and a live video texture extracted from multiple cameras. This 3D model is simple enough to be transmitted via a network. A prototype system has been developed and tested at actual soccer stadiums. A 3D video of a typical soccer scene, which includes more than a dozen players, was processed at video rate and transmitted to remote PCs through the internet at 15-24 frames per second.

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