Computer Vision & Pattern Recognition

In this research project, our focus is on developing automated computer vision techniques for measuring/recognizing spontaneous facial expressions and FACS action units and map onto expert human coding. We apply developed automated techniques to analyze emotion expressions of children with autism.

The focus of this research is to develop a novel holistic-based framework to estimate gaze direction. The gaze direction of an infant in a face-to-face interaction is classified as either 1) looking at the parent’s face or 2) looking away from the parent’s face.

Facial feature extraction is an important step in face recognition and is defined as the process of locating specific regions, points, landmarks, or curves/contours in a given 2-D image or a 3-D range image.

Most of the approaches that have been developed for 3-D face recognition are based on 3-D surface matching. I presented an approach for 3-D face recognition based on Ridge lines extracted from range facial images.

In the area of computer vision, optimization is an important issue. One of the new developed techniques that caught my attention recently is “Graph-Cut”. I worked on using this new optimization technique in applications such as image blending or image segmentation.

Video stabilization removes unwanted motion from video sequences, often caused by vibrations or other instabilities. This improves video viewability and can aid in detection and tracking in computer vision algorithms.

Most video surveillance systems can only provide simple event alarms such as Virtual Perimeter Breach, in which a person crosses an imaginary line (Below), or Abandoned Object Detection.