Dr. Ming Zhang's Research in Face Recognition using Artificial Neural Networks, Slides of Computer Science

Download Dr. Ming Zhang's Research in Face Recognition using Artificial Neural Networks and more Slides Computer Science in PDF only on Docsity!

FaceFlow: Face Recognition System

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.

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

PixelSmart Image Capture Card Source

Codes- Compiled & Linked!

Victor Image Processing Library

Running in Visual C++.NET!

BrainMaker Neural Network Software

the Fastest Training Package!

ExploreNet Neural Network Software

The Best Interface Package!

Edge Detection

Image Processing

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

Leshno, M. (1993)

“A standard multilayer feedforward network

with a locally bounded activation function

can approximate any continuous function to

any degree of accuracy if and only if the

network’s activation function is not a

polynomial”

Zhang, Ming (1995)

“ Consider a neural network piecewise function

group, in which each member is a standard

multilayer feedforward neural network, and

which has locally boundded, piecewise

continuous (rather than polynomial)

activation function and threshold. Eash such

group can approximate any king of piecewise

continuous function, and to any degree of

accuracy”