1. Introduction
Performance of metric learning is heavily dependent on features extracted
Sensitive to Performance of Filters used
Need to be robust to changes in orientation, scale, lighting and minor details (e.g. facial expression, glasses)
Proposed Solution: Gabor filter bank
Comparison with DFT filter bank
Tested with three different learning algorithms

2. Scope Extraction of features from face images using Gabor Filterbank
Face recognition with metric learning (three different metrics)
Performance Comparison with DFT

3. Gabor filter bank Gaussian profile modulated by sinusoidal plane wave
Optimal resolution in both spatial and frequency domains [4]
used with considerable success for image processing, classification and pattern recognition purposes ([11], [9], [1], [4])
robust against variations in illumination, rotation, scale and translation

4. Gabor Filters θ α β 8 orientations; 5 scales
center frequency of sinusoidal plane wave θ
orientation of Gaussian profile and plane wave α
sharpness parallel to plane wave β
sharpness perpendicular to plane wave
8 orientations; 5 scales

5. Designing the Gabor Filters
8 orientations; 5 scales
Theoretical Limit :
We choose :

6. How the Gabor Filters Look Like
Magnitudes
Real Parts

7. Gabor Filterbank Implementation

8. Gabor Filterbank Outputs

9. DFT-based Filterbank Implementation

10. Datasets Used Yale face dataset 165 greyscale images 15 individuals
11 images per subject
With glasses, happy, sad, sleepy, etc.
32x32 pixels
Yale face dataset

11. Datasets Used [contd.] AT&T face dataset 400 grayscale images
40 individuals
10 images per subject
Open/closed eyes, smiling/ not smiling, etc.
112x92 pixels
AT&T face dataset

12. Datasets Used [contd.] ORL face dataset 400 grayscale images
40 individuals
10 images per subject
Essentially same as AT&T, but each image cropped to 64x64 pixels
ORL face dataset

13. Experiment Design