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High Speed Face Recognition Based on Discrete Cosine Transforms and Neural Networks. Zhengjun Pan and Hamid Bolouri Department of Computer Science University of Hertfordshire Presented By Mustafa Mirac KOCATÜRK. OUTLINE. Introduction to the Face Recognition

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high speed face recognition based on discrete cosine transforms and neural networks

High Speed Face Recognition Based on Discrete Cosine Transforms and Neural Networks

Zhengjun Pan and Hamid Bolouri

Department of Computer Science

University of Hertfordshire

Presented By

Mustafa Mirac KOCATÜRK

High Speed Face Recognition Based on DCT and Neural Networks

outline
OUTLINE
  • Introduction to the Face Recognition
  • Existing Methods for Feature Extraction

and Advantages Using DCT

  • Key Characteristics of Recognition Systems
  • Information Packing Using DCT
  • System Description of DCT Recognition System
  • Brief Information about ORL Database
  • Experimental Simulations
  • Conclusion

High Speed Face Recognition Based on DCT and Neural Networks

introduction
INTRODUCTION
  • Face recognition is the science of programming a computer to recognize a human face.
  • The steps of Face Recognition are
  • Face Detection (Feature extraction)
  • Face Normalization
  • Face Identification

High Speed Face Recognition Based on DCT and Neural Networks

introduction1
INTRODUCTION
  • The Key Characteristics of the Recognition Systems are:
  • Recognition Rate
  • Training Time
  • Recognition Time

High Speed Face Recognition Based on DCT and Neural Networks

introduction2
INTRODUCTION
  • Existing Computational Models For Feature Extraction:
  • Geometrical Features
  • Statistical Features
  • Feature Points
  • Neural Networks

High Speed Face Recognition Based on DCT and Neural Networks

introduction3
INTRODUCTION
  • Problems of Existing Systems are:
  • High Information Redundancy
  • Building a Database of Faces
  • Computationally Expensive
  • Spare Computation Time for Real-Time Applications

High Speed Face Recognition Based on DCT and Neural Networks

introduction4
INTRODUCTION
  • The Advantages of DCT:
  • Removes the redundant info
  • Decreases the computational complexity
  • Much faster than the other models

High Speed Face Recognition Based on DCT and Neural Networks

discrete cosine transform
DISCRETE COSINE TRANSFORM

High Speed Face Recognition Based on DCT and Neural Networks

discrete cosine transform1
DISCRETE COSINE TRANSFORM
  • DCT is being used as a standard in JPEG files

High Speed Face Recognition Based on DCT and Neural Networks

discrete cosine transform2
DISCRETE COSINE TRANSFORM
  • How many coeffiecents should be used?

High Speed Face Recognition Based on DCT and Neural Networks

discrete cosine transform coefficient analysis
DISCRETE COSINE TRANSFORM(coefficient analysis)

High Speed Face Recognition Based on DCT and Neural Networks

discrete cosine transform coefficient analysis cont
DISCRETE COSINE TRANSFORM(coefficient analysis cont.)

High Speed Face Recognition Based on DCT and Neural Networks

discrete cosine transform subimage analysis
DISCRETE COSINE TRANSFORM(subimage analysis)

High Speed Face Recognition Based on DCT and Neural Networks

discrete cosine transform subimage analysis cont
DISCRETE COSINE TRANSFORM(subimage analysis cont.)

High Speed Face Recognition Based on DCT and Neural Networks

system description
SYSTEM DESCRIPTION
  • The main idea is to apply the DCT to reduce information redundancy and use the packed information for classification
  • System consists of
  • Coefficient Selection
  • Data Representation

High Speed Face Recognition Based on DCT and Neural Networks

orl database
ORL DATABASE
  • Built at Olivetti Research Laboratory
  • 400 images 10 for each 40 distinct objects
  • 4 female and 36 male subjects
  • 92 X 112 pixels each with 256 gray level
  • Images differ in;
  • Lightning
  • Facial expressions
  • Facial Details

High Speed Face Recognition Based on DCT and Neural Networks

simulations of dct experimental setup
SIMULATIONS OF DCT(experimental setup)
  • MLP are initialised to random values [-0.5,0.5]
  • Learning Parameters set to 0.02,0.008,0.0001
  • The max. number of training epochs is 1000
  • The multiplication factor of β is set to 1.1
  • Training samples are randomed to avoid the influence of the presentation order
  • 200 training and test images are used

(First 5 of the each 40 outputs are for

training and testing)

High Speed Face Recognition Based on DCT and Neural Networks

simulations of dct experimental setup cont
SIMULATIONS OF DCT(experimental setup cont.)
  • T-Tests are based on the 0.05 level of significance
  • T-Test statistics has to exceed 1.645 for experimental results to be classified as statistically different from the reference case.
  • The reference case of the system is
  • 35 DCT Coefficents
  • 75 Hidden Neurons

High Speed Face Recognition Based on DCT and Neural Networks

simulations of dct of coefficients
SIMULATIONS OF DCT(# of coefficients)

High Speed Face Recognition Based on DCT and Neural Networks

simulations of dct of hidden neurons
SIMULATIONS OF DCT(# of hidden neurons)

High Speed Face Recognition Based on DCT and Neural Networks

simulations of dct sub image size
SIMULATIONS OF DCT(sub-image size)

High Speed Face Recognition Based on DCT and Neural Networks

simulations of dct different recognition approaches
SIMULATIONS OF DCT(different recognition approaches)

High Speed Face Recognition Based on DCT and Neural Networks

conclusion
CONCLUSION
  • DCT using Neural Networks is a very fast and efficient approach in face recognition.
  • Truncating the unnecessary info reduces computational complexity.
  • The experiments reported above demonstrate that using only %0.34 of the DCT coefficients produces a respectable recognition rate while the processing time is 2 times faster.

High Speed Face Recognition Based on DCT and Neural Networks

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