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Stereo  matching  for  2d  face  recognition
 

Stereo matching for 2d face recognition

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    Stereo  matching  for  2d  face  recognition Stereo matching for 2d face recognition Presentation Transcript

    • DESIGN AND DEVELOPMENT FOR FACERECOGNITION USING STEREO MATCHING ALGORITHM by N.M.Harish BalajiSankara College of Science and Commerce
    • INTRODUCTION• Face Recognition(FR) - images & videos.• Face recognition compliments face detection .• Face detection - finds faces in images and videos .• Problems in FR - to handle pose variation .• 2 predominant methods 1) Geometric approach 2) Photometric approach
    • SECTIONS IN FACE RECOGNITIONFace Recognition deals with 3 main sections, they are: 1. Images with 3 landmarks in face. 2. Illumination variation. 3. Pose variation.
    • BRIEF PROCESS• FR handles pose & illumination variations.• Gallery image is generated with 4 landmark points.• Similarities are identified using matching cost.• Works well for large pose variations.• Dramatic changes is a challenging problem that an face recognition system needs to face.
    • FEATURES• Feature based system - detects - facial landmarks.• Initially face images need to be aligned. 1. To generate landmark points - Eyes, Nose, Mouth. 2. Fourth landmark - stereo.• Stereo - 3*3 filter - calculates the distance between test image & training image.
    • FACE RECOGNITION METHOD• Stereo Matching - supports good correspondence.• Dynamic programming - 2D face images.• Stereo algorithm - maximizes the cost function.
    • MATCHING PROCESS• Matching - individual pixel intensities.• Many matches .• Right match - difficult .
    • STEREO MATCHING• Stereo matching algorithm - individual pixel intensities.• Objective - Matching 2 images.• Matching - 2 scan lines l1 & l2.• Cost of matching is given by Matching cost = cost (l1,l2)
    • RECTIFICATION & MATCHING COST• Rectification - calculates similarity between images.• Recognition - matches images.
    • ILLUMINATION HANDLING• Quite difficult - more changes than in real image.• Chance of false detection.• To overcome - Normalization.
    • RESULTS• Performance is evaluated on real image.• Image contains - flat regions, shadings & texture.
    • RECOGNITION RATE To evaluate the performance of FR, recognition rate isusedRR= (No. of correctly identified face) (Total number of faces )
    • PERFORMANCE ANALYSIS METHOD RECOGNITION RATE LBP 82.50% LTP 80.00%NOVAL APPROACH 98.27%
    • BAR CHART REPRESENTATION RECOGNITION RATE1008060 RECOGNITION RATE4020 0 LBP LPT NOVEL APPROACH
    • CONCLUSION• Simple general method - reduces illumination changes.• Performance is good - accurate as well.• ADVANTAGE : Automatic face recognition system.