Neural Information Systems

FACEFLOW: Face Recognition System

ANSER :Rainfall Estimating System

THONN:Date Simulation System

Dr. Ming Zhang, Associate Professor

Department of Physics, Computer Science & Engineering

Dr. Ming Zhang

ANSER System Interface

PT-HONN Data Simulator

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

What Approved

Artificial Neural Network Techniques can :

- Can recognition one face in the laboratory using less than 1 second

- Currently can recognition about 1000 faces

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++. NetEnterprise Version!

PixelSmart Image Capture Card Source Codes- Compiled & Linked!

Victor Image Processing LibraryRunning in Visual C++.NET !

Faceflow: Face Model SimulatorTest Different Models!

BrainMaker Neural Network Software the Fastest Training Package!

ExploreNet Neural Network SoftwareThe Best Interface Package!

FERET Facial Image DatabaseStandard Face Database!

Research LabIn Modern Building !

We have a pattern recognition lab in the ARC building

We have our own room to do research.

Dr. Ming Zhang

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

Research Topics

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$2,000,000

Dr. Ming Zhang

Dr. Ming Zhang’ s Publications

(Face Recognition)

1 Journal Papers1)      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. 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, and John Fulcher, “ Face recognition using artificial neural network group-based adaptive tolerance (GAT) trees”, IEEE Transactions on Neural Networkis, vol. 7, no. 3, pp. 555-567, 1996.

 2 Patents 1)   Ming Zhang, et al, “Translation invariant face recognition using network adaptive

tolerance tree”, Australian Patent PM 1828, Oct. 14, 1993. 2) Ming Zhang, Ruli Wang, and Yiming Gong, “Standard nonlinear signal wave generator

based on the neural network”, Chinese Patents, No. 90 1 02857.6, May 17, 1990.

Dr. Ming Zhang

Dr. Ming Zhang’ s Publications (Face Recognition)3 Full Refereed Conference Papers1)      Shuxiang Xu, and Ming Zhang, A Novel Adaptive Activation Function, Accepted by IJCNN’2001 (International

Joint Conference on Neural Networks’ 2001), Washington DC, USA, July 2001, pp.2779 – 2782.2)      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, 25-26 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

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’2001 (International Joint Conference on Neural Networks’ 2001), Washington DC, USA, July 2001, pp. 1583 - 1587.

(3)  Ming Zhang, and BO Lu, Financial Data Simulation Using M-PHONN Model, Accepted by IJCNN’2001 (International Joint Conference on Neural Networks’ 2001), Washington DC, USA, July 2001, pp. 1828 - 1832.

(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’2001 (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, 2001.

Total 102 papers published

Dr. Ming Zhang

Why This Project?

1. Visual Studio.NET2. Image processing library 3. Image capture source codes4. New generation computer models and techniques5. Plenty of research topics6. Good support of software and hardware7. Strong support from our Department8. Experienced supervisor9. Paper to be published in the International Conference10. Big market

Dr. Ming Zhang

PT-HONN Data Simulator

Artificial Neural network expert System for Estimation of Rainfall from the satellite data

ANSER System (1991-2000)

- 1991-1992:US$66,000 suported by USA National Research Council & NOAA

- 1995-1996:A$11,000 suppouted by Australia Research Council& NOAA

- 1999-2000:US$62,000 suported by USA National Research Council & NOAA

Why Develop ANSER ?

- More than $3.5 billion in property is damaged and, more than 225 people are killed by heavy rain and flooding each year

- No rainfall estimating system in GIS system, No real time and working system of rainfall estimation in the world

- Can ANN be used in the weather forecasting area? If yes, how should we use ANN techniques in this area?

Why Use Neural Network Techniques ?

- Two Directions of New generation computer Quamtun Computer Artificial Neural Network- Much quicker speed ?- Complicated pattern recognition?- Unknown rule knowledge base?- Self learning reasoning network?- Super position for multip choice?

ANSER Rainfall Estimation Result

9th May 2000Time: 18Z

LAT LANMin 37.032 87.906Max 38.765 88.480

ANSERMin: 1.47 mmMax: 6.37mm

NAVY Min: 2.0mmMax: 6.0mm

Conclusion- What Approved

Artificial Neural Network Techniques can :

- Much quick speed: 5-10 time quick

- Unknown rule knowledge base: Rainfall

- Reasoning network: rainfall estimation

Conclusion- Next Step- Rebuild interface & retraining neural networks

- New neural netowrk models:

more complicated pattern recognition

- Self expending knowledge base:

attract knowledge from real time cases

- Self learning reasoning network: automatic system to

- Study in advance in 15 years: Artificial Neural Network - one of two directions of new generation computer Research

PHONN Simulator (1994 - 1996)- Polynomial Higher Order Neural Network financial data

simulator

- A$ 105,000 Supported by Fujitsu, Japan

THONN Simulator (1996 - 1998)- Trigonometric polynomial Higher Order Neural Network

financial data simulator

- A$ 10,000 Supported by Australia Research Council

PT-HONN Simulator (1999 - 2000)- Polynomial and Trigonometric polynomial Higher Order

Neural Network financial data simulator

- US$ 46,000 Supported by USA National Research Council

PT-HONN Data Simulator

Why Develop HONN ?

- No system can automatically simulate discontinue, unsmooth data very well

- No system can automatically find the perfect models for the discontinue, unsmooth data

Cloud Merge Using ANN Circle Operator

CONCLUSION- What Approved The results of the comparative experiments show

that THONG system is able to simulate higher frequency and higher order non-linear data, as well as being able to simulate discontinuous data.

The THONG model can not only be used for financial simulation, but also for financial prediction.

Complicated pattern recognition: cloud merger

Conclusion- Next Step- Rebuild interface & retraining neural networks

- New neural network models:

more complicated pattern recognition

- Financial data simulation experiments

- Rainfall data simulation experiments