1. 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

2. High Speed Face Recognition Based on DCT and Neural Networks
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

3. High Speed Face Recognition Based on DCT and Neural Networks
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

4. High Speed Face Recognition Based on DCT and Neural Networks
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

5. High Speed Face Recognition Based on DCT and Neural Networks
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

6. High Speed Face Recognition Based on DCT and Neural Networks
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

7. High Speed Face Recognition Based on DCT and Neural Networks
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

8. DISCRETE COSINE TRANSFORM
High Speed Face Recognition Based on DCT and Neural Networks

9. DISCRETE COSINE TRANSFORM
DCT is being used as a standard in JPEG files
High Speed Face Recognition Based on DCT and Neural Networks

10. DISCRETE COSINE TRANSFORM
How many coeffiecents should be used?
High Speed Face Recognition Based on DCT and Neural Networks

11. DISCRETE COSINE TRANSFORM (coefficient analysis)
High Speed Face Recognition Based on DCT and Neural Networks

12. DISCRETE COSINE TRANSFORM (coefficient analysis cont.)
High Speed Face Recognition Based on DCT and Neural Networks

13. DISCRETE COSINE TRANSFORM (subimage analysis)
High Speed Face Recognition Based on DCT and Neural Networks

14. DISCRETE COSINE TRANSFORM (subimage analysis cont.)
High Speed Face Recognition Based on DCT and Neural Networks

15. High Speed Face Recognition Based on DCT and Neural Networks
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

16. High Speed Face Recognition Based on DCT and Neural Networks
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

17. 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

18. SIMULATIONS OF DCT (experimental setup cont.)
T-Tests are based on the 0.05 level of significance
T-Test statistics has to exceed 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

19. SIMULATIONS OF DCT (# of coefficients)
High Speed Face Recognition Based on DCT and Neural Networks

20. SIMULATIONS OF DCT (# of hidden neurons)
High Speed Face Recognition Based on DCT and Neural Networks

21. SIMULATIONS OF DCT (sub-image size)
High Speed Face Recognition Based on DCT and Neural Networks

22. SIMULATIONS OF DCT (different recognition approaches)
High Speed Face Recognition Based on DCT and Neural Networks

23. High Speed Face Recognition Based on DCT and Neural Networks
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