00554196

00554196 - 114 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL....

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Unformatted text preview: 114 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 8, NO. 1, JANUARY 1997 Face Recognition/Detection by Probabilistic Decision-Based Neural Network Shang-Hung Lin, Sun-Yuan Kung, Fellow, IEEE, and Long-Ji Lin Abstract— This paper proposes a face recognition system based on probabilistic decision-based neural networks (PDBNN). With technological advance on microelectronic and vision system, high performance automatic techniques on biometric recognition are now becoming economically feasible. Among all the biomet- ric identification methods, face recognition has attracted much attention in recent years because it has potential to be most non- intrusive and user-friendly . The PDBNN face recognition system consists of three modules: First, a face detector finds the location of a human face in an image. Then an eye localizer determines the positions of both eyes in order to generate meaningful feature vectors. The facial region proposed contains eyebrows, eyes, and nose, but excluding mouth. (Eye-glasses will be allowed.) Lastly, the third module is a face recognizer . The PDBNN can be effectively applied to all the three modules. It adopts a hier- archical network structures with nonlinear basis functions and a competitive credit-assignment scheme. The paper demonstrates a successful application of PDBNN to face recognition applications on two public (FERET and ORL) and one in-house (SCR) databases. Regarding the performance , experimental results on three different databases such as recognition accuracies as well as false rejection and false acceptance rates are elaborated in Section IV-D and V. As to the processing speed , the whole recog- nition process (including PDBNN processing for eye localization, feature extraction, and classification) consumes approximately one second on Sparc10, without using hardware accelerator or co-processor. Index Terms— Decision-based neural network (DBNN), prob- abilistic DBNN, face detection, eye localization, virtual pattern generation, positive/negative training sets, hierarchical fusion, face recognition system. I. INTRODUCTION W ITH its emerging applications to secure access con- trol, financial transactions, etc., biometric recognition systems (e.g., face, palm, finger print) have recently taken on a new importance. With technological advance on mi- croelectronic and vision system, high performance automatic techniques on biometric recognition are now becoming eco- nomically feasible [3]. Among all the biometric identification methods, face recognition has attracted much attention in recent years because it has potential to be most nonintrusive and user-friendly . In this paper we propose an integrated face recognition system for security/surveillance purposes. This system involves three major tasks: 1) human face detection from still images and video sequences; 2) eye localization; Manuscript received February 15, 1996; revised June 19, 1996....
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00554196 - 114 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL....

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