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Neural Information Systems

FACEFLOW: Face Recognition System

ANSER :Rainfall Estimating System

THONN:Date Simulation System

ANSER System Interface

FACEFLOW (1992 - 2002)

A computer vision system for recognition of

3-dimensional moving faces using GAT model

(neural network Group-based Adaptive tolerance Tree)

• A$850,000 supported by SITA (Society Internationale

de Telecommunications Aeronautiques)

• A$40,500 supported by Australia Research Council
• A$78,000 supported by Australia Department of

Education.

• US$160,000 supported by USA National Research

Council.

Why Develop FACEFLOW?

• To use new generation computer technique,

artificial neural network, for developing

information systems.

• No real world face recognition system is running in

the world.

• Big security market
  • Biometric system
  • ID card identification system
  • Car and house security system

Next Step

• Rebuild interface for face recognition system
• Face Detection
  • Lighting
  • Background
  • Make up
• New neural network models
• More complicated pattern recognition
• Build a rear world face recognition System

Microsoft Visual C++. Net

Enterprise Version!

Victor Image Processing Library

Running in Visual C++.NET!

Faceflow: Face Model Simulator

Test Different Models!

ExploreNet Neural Network Software

The Best Interface Package!

FERET Facial Image Database

Standard Face Database!

Research Topics

• Neuron Network Group Models
• GAT Tree Model
  • real time and real world face recognition
• Neuron-Adaptive Neural Network Models
  • best match real world data
• Center Of Motion Model - motion center
• Second Order Vision Model - motion direction
• NAAT Tree Model - a possible more powerful model for

face recognition

Dr. Ming Zhang

• 11/1999 – 07/2000:

Senior USA NRC Research Associate

NOAA, Funding $70,000.

• 03/1995 – 11/1999: Ph.D. Supervisor

University of Western Sydney

Funding: A$203,724 Cash from Fujitsu, ARC, & UWSM

• 07/1994-03/1995: Ph.D. Supervisor and Lecturer

Monash University, A$50,000 Grant from Fujitsu)

• 11/1992-07/1994: Project Manager & P.H.D. Supervisor

University of Wollongong, (A$850,000 from SITA)

• 07/1991-10/1992: USA NRC Postdoctoral Fellow

NOAA, Funding: US$100,000)

• 07/1989-06/1991: Associate Professor and Postdoctoral Fellow

The Chinese Academy of the Sciences. Funding: RMB$2,000,

Dr. Ming Zhang Docsity.com

Dr. Ming Zhang’ s Publications

(Face Recognition)

3 Full Refereed Conference Papers 1) Shuxiang Xu, and Ming Zhang, A Novel Adaptive Activation Function, Accepted by IJCNN’ (International Joint Conference on Neural Networks’ 2001), Washington DC, USA, July 2001, pp.

2) Ming Zhang, Jing Chung Zhang, John Fulcher, "Neural network group models for data approximation", International Journal of Neural Systems, Vol. 10, No. 2, April, 2000, pp. 123-142.

3. Ming Zhang, Shuxiang Xu, and Bo Lu, “Neuron-adaptive higher order neural network group models”, in Proceedings of IJCNN’99 , Washington, D.C., USA, July 10-16, 1999.
4. Ming Zhang, Shuxiang Xu, Nigel Bond, and Kate Stevens, “Neuron-adaptive feedforward neural network group models”, in Proceedings of IASTED International Conference on Artificial Intelligence and Soft Computing , Honolulu, Hawaii, USA, August 9-12, 1999, pp.281-284.
5. John Fulcher, Ming Zhang, “Translation-invariant face recognition using the parellel NAT-tree neural network model”, in Proceedings of Parallel ComputingWorkshop 1997 , Canberra, Australia, 25- September, 1997, pp. P1-U-1 – P1-U-1-4.
6. Ming Zhang, John Fulcher, “Face recognition system using NAT tree”, in Proceedings of IASTED International Conference on Artificial Intelligence and Soft Computing , Banff, Canada, July 27 - August 1, 1997, pp. 244-247.
7. Ming Zhang, and John Fulcher, “Face perspective understanding using artificial neural network group- based tree”, in Proceedings of International Conference on Image Processing , Lausanne, Switzerland, vol III, September 16-19, 1996, pp.475-478.
8. Ming Zhang, and John Fulcher, “Translation invariant face recognition using a network adaptive tolerance tree”, in Proceedings of World Congress On Neural Networks , San Diego, California, USA, September 15 -18, 1996, pp

Dr. Ming Zhang Docsity.com

Dr. Ming Zhang’ s Publications

Year 2001

(1) Hui Qi, Ming Zhang, and Roderick Scofield, Rainfall Estimation Using M-PHONN Model, Accepted by IJCNN’2001 (International Joint Conference on Neural Networks’ 2001), Washington DC, USA, July 2001, pp. 1620 - 1624.

(2) Ming Zhang, and Roderick Scofield, Rainfall Estimation Using A-PHONN Model, Accepted by IJCNN’ (International Joint Conference on Neural Networks’ 2001), Washington DC, USA, July 2001, pp. 1583 -

(3) Ming Zhang, and BO Lu, Financial Data Simulation Using M-PHONN Model, Accepted by IJCNN’ (International Joint Conference on Neural Networks’ 2001), Washington DC, USA, July 2001, pp. 1828 -

(4) Ming Zhang, Financial Data Simulation Using A-PHONN Model, Accepted by IJCNN’2001 (International Joint Conference on Neural Networks’ 2001), Washington DC, USA, July 2001, pp.1823 - 1827.

(5) Shuxiang Xu, and Ming Zhang, A Novel Adaptive Activation Function, Accepted by IJCNN’ (International Joint Conference on Neural Networks’ 2001), Washington DC, USA, July 2001, pp.2779 – 2782 (6) Ming Zhang, Rex Gantenbein, Sung Y. Shin, and Chih-Cheng Hung, The application of artificial neural networks in knowledge-based information systems, International Journal of Computer and Information Science, Vol 2, No.2, 2001, pp.49 - 58.

(7) Ming Zhang, Shuxiang Xu, and John Fulcher, Neuron-Adaptive Higher Order Neural Network Models

for Automated Financial Data Modeling”, Accepted by IEEEE transactions on Neural Networks, July,

Total 102 papers published Dr. Ming Zhang Docsity.com