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

22.04.2322.04.23 High Speed Face Recognition Based oHigh Speed Face Recognition Based on DCT and Neural Networksn DCT and Neural Networks

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

Zhengjun Pan and Hamid BolouriZhengjun Pan and Hamid BolouriDepartment of Computer ScienceDepartment of Computer Science

University of HertfordshireUniversity of Hertfordshire

Presented ByPresented ByMustafa Mirac KOCATÜRKMustafa Mirac KOCATÜRK


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OUTLINEOUTLINE

Introduction to the Face RecognitionIntroduction to the Face RecognitionExisting Methods for Feature ExtractionExisting Methods for Feature Extractionand Advantages Using DCTand Advantages Using DCTKey Characteristics of Recognition SystemsKey Characteristics of Recognition SystemsInformation Packing Using DCTInformation Packing Using DCTSystem Description of DCT Recognition SystemSystem Description of DCT Recognition SystemBrief Information about ORL DatabaseBrief Information about ORL DatabaseExperimental SimulationsExperimental SimulationsConclusionConclusion


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INTRODUCTIONINTRODUCTION

Face recognitionFace recognition is the science of is the science of programming a computer to recognize a programming a computer to recognize a human face.human face.The steps of Face Recognition areThe steps of Face Recognition are

1.1. Face Detection (Feature extraction)Face Detection (Feature extraction)2.2. Face NormalizationFace Normalization3.3. Face IdentificationFace Identification


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The Key Characteristics of the The Key Characteristics of the Recognition Systems are:Recognition Systems are:

1.1. Recognition RateRecognition Rate2.2. Training TimeTraining Time3.3. Recognition TimeRecognition Time


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Existing Computational Models For Existing Computational Models For Feature Extraction:Feature Extraction:

1.1. Geometrical FeaturesGeometrical Features2.2. Statistical FeaturesStatistical Features3.3. Feature PointsFeature Points4.4. Neural NetworksNeural Networks


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Problems of Existing Systems are:Problems of Existing Systems are:

1.1. High Information RedundancyHigh Information Redundancy2.2. Building a Database of FacesBuilding a Database of Faces3.3. Computationally ExpensiveComputationally Expensive4.4. Spare Computation Time for Real-Time Spare Computation Time for Real-Time

ApplicationsApplications


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The Advantages of DCT:The Advantages of DCT:

1.1. Removes the redundant infoRemoves the redundant info2.2. Decreases the computational complexityDecreases the computational complexity3.3. Much faster than the other modelsMuch faster than the other models


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DISCRETE COSINE TRANSFORMDISCRETE COSINE TRANSFORM


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DCT is being used as a standard in JPEG filesDCT is being used as a standard in JPEG files


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How many coeffiecents should be used?How many coeffiecents should be used?


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DISCRETE COSINE TRANSFORMDISCRETE COSINE TRANSFORM(coefficient analysis)(coefficient analysis)


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DISCRETE COSINE TRANSFORMDISCRETE COSINE TRANSFORM(coefficient analysis cont.)(coefficient analysis cont.)


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DISCRETE COSINE TRANSFORMDISCRETE COSINE TRANSFORM(subimage analysis)(subimage analysis)


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DISCRETE COSINE TRANSFORMDISCRETE COSINE TRANSFORM(subimage analysis cont.)(subimage analysis cont.)


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SYSTEM DESCRIPTIONSYSTEM DESCRIPTIONThe main idea is to apply the DCT to reduce The main idea is to apply the DCT to reduce information redundancy and use the packed information redundancy and use the packed information for classificationinformation for classificationSystem consists of System consists of

1.1. Coefficient SelectionCoefficient Selection2.2. Data RepresentationData Representation


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ORL DATABASEORL DATABASE

Built at Olivetti Research LaboratoryBuilt at Olivetti Research Laboratory400 images 10 for each 40 distinct objects400 images 10 for each 40 distinct objects4 female and 36 male subjects4 female and 36 male subjects92 X 112 pixels each with 256 gray level92 X 112 pixels each with 256 gray levelImages differ in;Images differ in;

1.1. LightningLightning2.2. Facial expressionsFacial expressions3.3. Facial DetailsFacial Details


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SIMULATIONS OF DCTSIMULATIONS OF DCT(experimental setup)(experimental setup)

MLP are initialised to random values [-0.5,0.5]MLP are initialised to random values [-0.5,0.5]Learning Parameters set to 0.02,0.008,0.0001Learning Parameters set to 0.02,0.008,0.0001The max. number of training epochs is 1000The max. number of training epochs is 1000The multiplication factor of The multiplication factor of ββ is set to 1.1 is set to 1.1Training samples are randomed to avoid the Training samples are randomed to avoid the influence of the presentation orderinfluence of the presentation order200 training and test images are used200 training and test images are used(First 5 of the each 40 outputs are for (First 5 of the each 40 outputs are for training and testing) training and testing)


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SIMULATIONS OF DCTSIMULATIONS OF DCT(experimental setup cont.)(experimental setup cont.)

T-Tests are based on the 0.05 level of T-Tests are based on the 0.05 level of significancesignificanceT-Test statistics has to exceed 1.645 for T-Test statistics has to exceed 1.645 for experimental results to be classified as experimental results to be classified as statistically different from the reference statistically different from the reference case.case.The reference case of the system isThe reference case of the system is

1.1. 35 DCT Coefficents35 DCT Coefficents2.2. 75 Hidden Neurons75 Hidden Neurons


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SIMULATIONS OF DCTSIMULATIONS OF DCT(# of coefficients)(# of coefficients)


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SIMULATIONS OF DCTSIMULATIONS OF DCT(# of hidden neurons)(# of hidden neurons)


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SIMULATIONS OF DCTSIMULATIONS OF DCT(sub-image size)(sub-image size)


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SIMULATIONS OF DCTSIMULATIONS OF DCT(different recognition approaches)(different recognition approaches)


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CONCLUSIONCONCLUSION

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

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

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