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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. OUTLINE. Introduction to the Face Recognition

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

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  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. 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. 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. 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. 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. 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. 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. 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. 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 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

  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. 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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