Television – Camera – system and detail – Combined image signal generator and general image signal...
Reexamination Certificate
1997-05-21
2001-02-06
Ho, Tuan (Department: 2712)
Television
Camera, system and detail
Combined image signal generator and general image signal...
C382S190000, C382S118000, C348S014160
Reexamination Certificate
active
06184926
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention generally relates to real-time video image analysis, and more specifically to the detection of human faces and eyes within real-time video images.
2. Description of the Prior Art
In recent years, the detection of human faces from video data has become a popular research topic. There are numerous commercial applications of face detection, such as in face recognition, verification, classification, identification as well as security access and multimedia. To extract the human face in an uncontrolled environment, most prior art techniques attempt to overcome the difficulty of dealing with issues such as variations in lighting, variations in pose, occlusion of people by other people, and cluttered or non-uniform backgrounds.
In one prior art face detection technique, an example-based learning approach for locating unoccluded human frontal faces is used. The approach measures a distance between the local image and a few view-based “face” and “non face” pattern prototypes at each image location to locate the face. In another technique, the distance to a “face space”, defined by “eigenfaces”, is used to locate and track frontal human faces. In yet another prior art technique, human faces are detected by searching for significant facial features at each location in the image. Finally, in other techniques, a deformable template based approach is used to detect faces and to extract facial features.
In addition to the detection of faces within video image sequences, prior art systems have attempted to detect eyes on human heads. For example, Challepa et al., “Human and Machine Recognition of Faces: A Survey”,
Proceedings of the IEEE,
vol. 83, no. 5, pp. 705-740, May 1995, described a process for detecting eyes on a human head, where the video image includes a front view of the head. For frontal views, eye detection that is based on geometrical measures has been extensively studied, by, for example, Stringa, “Eyes Detection for Face Recognition”,
Applied Artificial Intelligence,
vol. 7, no. 4, pp. 365-382, October-December 1993 and Brunelli et al., “Face Recognition: Features versus Templates”,
IEEE Transaction on Pattern Analysis and Machine Intelligence,
October 1993. Additionally, Yuilee et al., “Feature Extraction from Faces Using Deformable Templates”,
International Journal of Computer Vision,
vol. 8, pp. 299-311, 1992, describe a deformable template-based approach to facial feature detection. However, these methods may lead to significant problems in the analysis of profile or back views. Moreover, the underlying assumption of dealing only with frontal faces is simply not valid for real-world applications.
There is therefore a significant need in the art for a system that can quickly, reliably and flexibly detect the existence of a face or faces within a video image, and that can also extract various features of each face, such as eyes.
SUMMARY OF THE INVENTION
The present invention provides for the detection of human heads, faces and eyes in real-time and in uncontrolled environments. The present invention may be implemented with commercially available components, such as a standard video camera and a frame grabber, on a personal computer (PC) platform. The approach used by the present invention is based on a probabilistic framework that uses a deformable template model to describe the human face. The present invention works both with simple head-and-shoulder video sequences, as well as with complex video scenes with multiple people and random motion. The present invention is able to locate the eyes from different head poses (rotations in image plane as well as in depth). The information provided by the location of the eyes may be used to extract faces from a frontal pose in a video sequence. The extracted frontal frames can be passed to recognition and classification systems (or the like) for further processing.
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Proceedings of the 13thInternational Conference on Pattern Recognition, etc.,Face Detection and Facial Feature Extraction Using Color, Shape and Symmetry-Based Cost Functions, Saber et al., 1996, pp. 654, 658.
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IBM Technical Disclosure Bulletin,Method for Extracting Facial Features by Using Color Information, Oct. 1, 1995, vol. 38, No. 10, pp. 163-165.
Khosravi Mehdi
Nefian Ara V.
Ho Tuan
NCR Corporation
Needle & Rosenberg P.C.
Wilson Jacqueline
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