Face recognition/detection by probabilistic decision-based neural network

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 biometric identification methods, face recognition has attracted much attention in recent years because it has potential to be most nonintrusive 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 hierarchical 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. As to the processing speed, the whole recognition process (including PDBNN processing for eye localization, feature extraction, and classification) consumes approximately one second on Sparc10, without using hardware accelerator or co-processor.

[1]  Sun-Yuan Kung,et al.  Decision-based neural network for face recognition system , 1995, Proceedings., International Conference on Image Processing.

[2]  B. Miller,et al.  Vital signs of identity [biometrics] , 1994, IEEE Spectrum.

[3]  Bruce W. Suter,et al.  The multilayer perceptron as an approximation to a Bayes optimal discriminant function , 1990, IEEE Trans. Neural Networks.

[4]  Sun-Yuan Kung,et al.  Probabilistic DBNN via expectation-maximization with multi-sensor classification applications , 1995, Proceedings., International Conference on Image Processing.

[5]  Abhijit S. Pandya,et al.  Pattern Recognition with Neural Networks in C++ , 1995 .

[6]  Ingemar J. Cox,et al.  Feature-based face recognition using mixture-distance , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Alex Pentland,et al.  Probabilistic visual learning for object detection , 1995, Proceedings of IEEE International Conference on Computer Vision.

[8]  Terrence J. Sejnowski,et al.  SEXNET: A Neural Network Identifies Sex From Human Faces , 1990, NIPS.

[9]  Roberto Brunelli,et al.  Face Recognition: Features Versus Templates , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Joachim M. Buhmann,et al.  Distortion Invariant Object Recognition in the Dynamic Link Architecture , 1993, IEEE Trans. Computers.

[11]  Andy Harter,et al.  Parameterisation of a stochastic model for human face identification , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[12]  Ferdinando Silvestro Samaria,et al.  Face recognition using Hidden Markov Models , 1995 .

[13]  Jonathan Phillips,et al.  Matching pursuit filters applied to face identification , 1994, Optics & Photonics.

[14]  Narendra Ahuja,et al.  Learning recognition and segmentation of 3-D objects from 2-D images , 1993, 1993 (4th) International Conference on Computer Vision.

[15]  Ke Liu,et al.  A robust algebraic method for human face recognition , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems.

[16]  Sun-Yuan Kung,et al.  A probabilistic decision-based neural network for locating deformable objects and its applications to surveillance system and video browsing , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[17]  Richard Lippmann,et al.  Neural Network Classifiers Estimate Bayesian a posteriori Probabilities , 1991, Neural Computation.

[18]  Rama Chellappa,et al.  Human and machine recognition of faces: a survey , 1995, Proc. IEEE.

[19]  Takeo Kanade,et al.  Human Face Detection in Visual Scenes , 1995, NIPS.

[20]  Yasuhito Suenaga,et al.  Robust face identification scheme: KL expansion of an invariant feature space , 1992, Other Conferences.

[21]  Arcot Sowmya,et al.  Neural network approach to component versus holistic recognition of facial expressions in images , 1992, Other Conferences.

[22]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[23]  F. Girosi,et al.  Networks for approximation and learning , 1990, Proc. IEEE.

[24]  S. Kung,et al.  A probabilistic DBNN with applications to sensor fusion and object recognition , 1995, Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing.

[25]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[26]  M. Bichsel.  Strategies of robust object recognition for the automatic identification of human faces , 1991 .

[27]  Michael David Kelly,et al.  Visual identification of people by computer , 1970 .

[28]  Sun-Yuan Kung,et al.  A neural network approach to face/palm recognition , 1995, Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing.

[29]  Alex Pentland,et al.  View-based and modular eigenspaces for face recognition , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Tomaso A. Poggio,et al.  Learning Human Face Detection in Cluttered Scenes , 1995, CAIP.

[31]  Sun-Yuan Kung,et al.  Decision-based neural networks with signal/image classification applications , 1995, IEEE Trans. Neural Networks.

[32]  Ah Chung Tsoi,et al.  Face recognition: a convolutional neural-network approach , 1997, IEEE Trans. Neural Networks.