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DOOR ENTRY SYSTEM

DOOR ENTRY SYSTEM. Team:. Alina Dinca László Papp Adrian Ulges Csaba Domokos Cercel Constantin. What is Project 9 about?. Name: Door entry system – feature analysis of a face using point separation Input: images of several faces

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DOOR ENTRY SYSTEM

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  1. DOOR ENTRY SYSTEM Team: Alina Dinca László Papp Adrian Ulges Csaba Domokos Cercel Constantin

  2. What is Project 9 about? • Name: Door entry system – feature analysis of a face using point separation • Input: images of several faces • Operation: Identify key points (eyes, end of nose, mouth). Measure distances and angles between these (for different orientations). Feed the results into a statistical analysis routine. Identify for unknown image most likely match. • Coding: C++, Matlab • Remarks: difficulty quite hard

  3. What we have • Data base with grayscale pictures in the .pgm format

  4. What we want to achieve • Locate the key points • Make a classification

  5. Step 1 Locating key points • algorithm for .pgm reader • extract 64/64 keypoint cut-outs • make an average (pattern) for each group of cut-outs

  6. Idea1. Using FFT => didn’t work!  • transform the patterns .pgms with Fast Fourier Transformation • transform the input image with Fast Fourier Transformation • convolute the input image with each pattern to find the maximum • transform them back from the Fourier space FFT * ( ) Inverse FFT FFT Response image

  7. Idea 2. Similarity measure: correlation • The formulafor it is: from {-1, 1}. If almost 1, then we have a match!! • Get the maximum • Slow algorithm  (2½ minutes) Correlation image maximum -1 -1 1 1

  8. Idea 2. => Hierarchical Matching --- A faster aproach --- • Scaling the input and the average twice • Match in small image • Find the match and scale back the match • Faster algorithm (6 seconds) 2nd scaling 1st scaling Input 2nd scaling 1st scaling Average 64/64

  9. Evaluation: • 10 pictures from the data base • search eyes, noses, lips • visual inspection • Results • eye - 80% • nose - 80% • lip - 20% • Side knowledge about keypoints?

  10. Step 2 Make the classification • use 20 key points from Data Base • feature vectors: normalized coordinates • (form a neuronal network) • use the nearest neighbour Evaluation: - 1020 data records - 510 training set - 510 test set - results: 98% recognition rate -1 1 -1 ( ) Training 1 Acces granted Acces denied New image

  11. THANK YOU…  … for your attention

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