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 03.12.2018 High Speed Face Recognition Based on DCT and Neural Networks

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 03.12.2018 High Speed Face Recognition Based on DCT and Neural Networks

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 03.12.2018 High Speed Face Recognition Based on DCT and Neural Networks

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 03.12.2018 High Speed Face Recognition Based on DCT and Neural Networks

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 03.12.2018 High Speed Face Recognition Based on DCT and Neural Networks

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 03.12.2018 High Speed Face Recognition Based on DCT and Neural Networks

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 03.12.2018 High Speed Face Recognition Based on DCT and Neural Networks

DISCRETE COSINE TRANSFORM 03.12.2018 High Speed Face Recognition Based on DCT and Neural Networks

DISCRETE COSINE TRANSFORM DCT is being used as a standard in JPEG files 03.12.2018 High Speed Face Recognition Based on DCT and Neural Networks

DISCRETE COSINE TRANSFORM How many coeffiecents should be used? 03.12.2018 High Speed Face Recognition Based on DCT and Neural Networks

DISCRETE COSINE TRANSFORM (coefficient analysis) 03.12.2018 High Speed Face Recognition Based on DCT and Neural Networks

DISCRETE COSINE TRANSFORM (coefficient analysis cont.) 03.12.2018 High Speed Face Recognition Based on DCT and Neural Networks

DISCRETE COSINE TRANSFORM (subimage analysis) 03.12.2018 High Speed Face Recognition Based on DCT and Neural Networks

DISCRETE COSINE TRANSFORM (subimage analysis cont.) 03.12.2018 High Speed Face Recognition Based on DCT and Neural Networks

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 03.12.2018 High Speed Face Recognition Based on DCT and Neural Networks

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 03.12.2018 High Speed Face Recognition Based on DCT and Neural Networks

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) 03.12.2018 High Speed Face Recognition Based on DCT and Neural Networks

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 03.12.2018 High Speed Face Recognition Based on DCT and Neural Networks

SIMULATIONS OF DCT (# of coefficients) 03.12.2018 High Speed Face Recognition Based on DCT and Neural Networks

SIMULATIONS OF DCT (# of hidden neurons) 03.12.2018 High Speed Face Recognition Based on DCT and Neural Networks

SIMULATIONS OF DCT (sub-image size) 03.12.2018 High Speed Face Recognition Based on DCT and Neural Networks

SIMULATIONS OF DCT (different recognition approaches) 03.12.2018 High Speed Face Recognition Based on DCT and Neural Networks

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. 03.12.2018 High Speed Face Recognition Based on DCT and Neural Networks