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

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

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

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

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

( ) 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 Idea1. Using FFT => didn’t work!  FFT * Response image Inverse FFT

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

2 nd scaling 1 st scaling 1) Scaling the input and the average twice 2) Match in small image 3) Find the match and scale back the match 4) Faster algorithm (6 seconds) 2 nd scaling 1 st scaling Idea 2. => Hierarchical Matching --- A faster aproach --- Input Average 64/64

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

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

THANK YOU… … for your attention