CSE / 60537: Biometrics

Transcription

1 CSE / 60537: Biometrics * ** * * ** * * Face Recognition 8 174

2 ! The Computer Vision Research Lab is seeking participants for 3-D Biometrics research! The CVRL is investigating the performance of the latest technology in 3-D face recognition. We are in need of subjects who are willing to attend two separate minute sessions where multiple 3-D iface mages will be taken. All subjects are eligible for up to a $15.00 Domer Dollar credit for their time: $5.00 will be credited to your account after the first visit and an additional $10.00 credit will be posted after your second visit. Location: 355C Fitzpatrick Time: Please schedule an appointment by going to: Or using the QR code below. Appointments are available at the following times through November 20: Tuesday 8:00 AM 6:00 PM Wednesday 9:00 AM 6:00 PM Thursday : 9:00AM 1:40 PM Friday: 10:00 AM 1:00 PM Eligibility: Participants must be between the ages of 18 & 65. You must have a valid ND ID card with you at the time of registration. Participants who came to two sessions in the Spring of 2015 are not eligible to participate again. 175

3 Face Recognition ( Hand-tuned Features ) 176

4 Inspiration from biology 177

5 Cortical Area V1 Ventral-dorsal streams BY 3.0 Selket Cortex visuel de l'homme aires V1, V2 et V3 BY-SA 3.0 Wandell, Dumoulin, Winawer Ventral Stream 178

6 Direction Selectivity Single cell recordings Hubel+wiesel BY-SA 3.0 Jimmierock 179

7 Gabor Filters Gabor filter-type receptive field Inhibition Facilitation Gabor filter BY-SA 3.0 Joe pharos 180

8 Gabor Filters Receptive fields (a) odd and (b) even Gabor filters T.S. Lee IEEE T-PAMI

9 Gabor Filters In 1D Sinusoidal Gaussian Gabor filter Image credit: 182

10 Gabor Filters 2D Gabor filter kernel (real component) g(x, y; phase offset wavelength s.d. of the Gaussian envelope orientation x y 02,,,, )=exp cos 2 2 spatial aspect ratio 2 x0 + where x 0 = xcos + ysin y 0 = xsin + ycos and 183

11 Filtering a Face Image credit: Tsung-Han Hsieh ( 184

12 Not the whole story Felleman and Van Essen Cerebral Cortex

13 V1-like features Pinto et al. CVPR Locally normalize the image via 3x3 neighborhoods 2. Apply 96 spatially local Gabor wavelets to image 3. Normalize output values via 3x3 neighborhoods 4. Threshold output values - Values < 0 clipped to 0 - Values > 1 clipped to 1 186

14 96 Gabor Filters 16 Orientations around the clock Six spatial frequencies: 1/2, 1/3, 1/4, 1/6, 1/11, 1/18, 1/23, 1/35 cycles/pixel 43x43 pixels, 1 pixel stride 187

15 V1-Like Features on LFW V1-like 188

16 Elastic Bunch Graph Matching (EBGM) L. Wiskott et al. T-PAMI 1997 Image credit: Rolf P. Würtz 189

17 EBGM: Gabor Jets Representation of local texture Image credit: Rolf P. Würtz 190

18 EBGM: Graphs Vertices: Jets Graph comparison: Image credit: Rolf P. Würtz vertex similarity edge similarity 191

19 EBGM: Elastic Graph Matching Model Graph G M Image credit: Rolf P. Würtz 192

20 EBGM: Bunch Graphs What do we do if we don t have G M? Consider Jets from a gallery of many people. local expert i.e., eye jets from person 1, the nose jet from person 2, and the mouth jet from person 3. Find best fitting Jet at each node via: 193

21 EBGM: Face Recognition Assume we have 1,000 images 1. Build a face graph - Manually define fiducial points on one face to establish graph 2. Build a face bunch graph - Increase reliability with the first 100 images 3. Build the model gallery of graphs - Remaining 900 images can be processed automatically 194

22 EBGM: Face Recognition 4. Build a the probe graph - Same process used for gallery applies here 5. Comparison with all model graphs - Generates 1000 similarity scores 6. Recognition - Choose best similarity score and apply a threshold to determine if it s a valid match 195

23 EBGM Results Beveridge et al. MVA 2005 EBGM DUP1 DUP2 EBGM FERET DUP1 FERET DUP2 196

24 Scale Invariant Feature Transform (SIFT) Desirable properties for a feature descriptor: Invariant to Scale SCface Invariant to Rotation Lowe ICCV 1999 Li et al. CLEAR workshop 2007 Adapted from notes by L. Shapiro 197

25 SIFT Matching 198

26 Advantages of SIFT Locality: features are local Distinctiveness: features can be matched to a large database Quantity: many features can be generated for small faces Efficiency: close to realtime performance Extensibility: can easily be extended to a wide range of feature types 199

27 SIFT Procedure 1. Scale-space extrema detection Search over multiple scales and image locations 2. Keypoint localization Pick stable points by fitting a model to determine location and scale 3.Orientation assignment Compute best orientation(s) for each keypoint region 4. Keypoint description Local image gradients at selected scale and rotation 200

28 Scale-space Extrema Detection Start with a V1-like feature: Approximate for speed: Laplacian of Gaussian Kernel Good blob detector 201

29 Scale-space Extrema Detection s+3 images including original s+2 difference images Image credit: D. Lowe s determines the number of images per octave 202