Abstract: The present invention relates to a figure recognition system and method for automatic detection, tracking and recognition of a human face image. 2D image data in the surveillance zone are remotely collected by using an optical sensor, the faces of all persons in the surveillance zone are detected, and corresponding positions are determined. The face is detected, the detected face's feature coordinate is estimated, and the detected face and the feature are tracked in the next frame while processing the video sequence. Image quality of each detected face is determined according to parameters of focus, brightness, contrast, and the presence of glasses. Recognition methods stored in the repository for each detected face are adjusted by considering the face image quality computation value, and a biometric feature set is generated by using the recognition method selected for each detected face.

Abstract: The present invention relates to a figure recognition system and method for automatic detection, tracking and recognition of a human face image. 2D image data in the surveillance zone are remotely collected by using an optical sensor, the faces of all persons in the surveillance zone are detected, and corresponding positions are determined. The face is detected, the detected face's feature coordinate is estimated, and the detected face and the feature are tracked in the next frame while processing the video sequence. Image quality of each detected face is determined according to parameters of focus, brightness, contrast, and the presence of glasses. Recognition methods stored in the repository for each detected face are adjusted by considering the face image quality computation value, and a biometric feature set is generated by using the recognition method selected for each detected face.

Abstract: Image fragments are formed in regions corresponding to circles searched from an input image. In a cascade of homogeneous classifiers, each classifier classifies input vectors corresponding to the image fragments into a face type and a non-face type. This procedure is performed on all images included in an image pyramid and the coordinates of a face detected based on the results of the procedures on all images.

Abstract: A method and apparatus for recognizing a protrusion on a face is proposed. The apparatus forms two images, wherein one is formed in a condition in which a target is lighted and another is formed in a condition in which the target is non-lighted. Face regions are detected from the images respectively and then are compared each other. Then, analyzing change in intensity for indicating change in contrast of the compared regions is performed. Based on the result of the analyzing, it is determined whether there is a protrusion in a face presented in the images.

Abstract: Image fragments are formed in regions corresponding to circles searched from an input image. In a cascade of homogeneous classifiers, each classifier classifies input vectors corresponding to the image fragments into a face type and a non-face type. This procedure is performed on all images included in an image pyramid and the coordinates of a face detected based on the results of the procedures on all images.

Abstract: A method and apparatus for recognizing a protrusion on a face is proposed. The apparatus forms two images, wherein one is formed in a condition in which a target is lighted and another is formed in a condition in which the target is non-lighted. Face regions are detected from the images respectively and then are compared each other. Then, analyzing change in intensity for indicating change in contrast of the compared regions is performed. Based on the result of the analyzing, it is determined whether there is a protrusion in a face presented in the images.

Abstract: An image quality control system and method is disclosed. At least one infrared camera takes a screen image of a room. When there are a plurality of cameras, images of the cameras are synchronized with respect to time, and a specific object of the image is tracked to estimate image quality of the object. When there are a plurality of cameras, a 3D screen model is reconfigured, and positions of the cameras and the infrared lighting tools are controlled. Infrared lighting and the cameras are controlled, and particularly, optical axis direction, optical magnification, exposure time, and the iris of the camera can be amended. Next, a high-quality object image list can be generated so as to process the images.