A probabilistic decision-based neural network for locating deformable objects and its applications to surveillance system and video browsing
Shang‐Hung Lin, Yin Chan, Sun‐Yuan Kung · 2002
Detection of a (deformable) pattern or object is an important machine learning and computer vision problem. The task involves finding specific (but locally deformable) patterns in images, such as human faces and eyes/mouths. There are many important commercial applications. This paper presents a decision-based neural network for finding such patterns with specific applications to detecting human faces and locating eyes in the faces. The system built upon the proposal has been demonstrated to be applicable under reasonable variations of orientation and/or lighting, and with the possibility of eye glasses. This method has been shown to be very robust against a large variation of face features and eye shapes. The algorithm takes only 200 ms on a SUN Sparc20 workstation to find human faces in an image with 320/spl times/240 pixels. For a facial image with 320/spl times/240 pixels, the algorithm takes 500 ms to locate two eyes on a SUN Sparc20 workstation. Furthermore, the algorithm can be easily implemented via specialised hardware for real time performance. We have applied this technique to two applications (surveillance system, video browsing) and this paper provides experimental results. Although we have only shown its successful implementation on face detection and eye localization, the proposed technique is meant for more general applications of detection of any (locally deformable) object.