1. Face Recognition
Monday, February 1, 2016

2. Motivation
Overview of Methods
Face Detection
Face Alignment
Face Representation
Face Classifier
Results

3. Motivation: General Goal
Given a picture of a person’s face
Given a bag of possible names
What’s the name of the person in the picture?
Goal 2:
Given two pictures of a person’s face
Are these of the same person?

4. Motivation
Overview of Methods
Face Detection
Face Alignment
Face Representation
Face Classifier
Results

5. Overview of Methods Face Detection Face Alignment Feature Extraction
Localize the face
Face Alignment
Factor out 3D transformation
Feature Extraction
Find compact representation
Classification
Answer the question

6. Motivation
Overview of Methods
Face Detection
Face Alignment
Face Representation
Face Classifier
Results

7. Methods for Detection Cascaded Ada-boosting Deep Neural Net
[P Viola 01]
Deep Neural Net
[M Osadchy 07]

8. Motivation
Overview of Methods
Face Detection
Face Alignment
Face Representation
Face Classifier
Results

9. Challenges in Face Alignment
Infer 3D from 2D
Slight occlusion
Lighting condition
Head orientation
Non rigid deformation

10. DeepFace Alignment: Substep 1
2D feature point extraction
2D alignment 𝑥 𝑎𝑛𝑐ℎ𝑜𝑟 =(𝑆∗𝑅∗𝑇) 𝑥 𝑠𝑜𝑢𝑟𝑐𝑒
Only for in plane alignment
Fiducial Point Detection
2D Transformation
Until convergence

11. DeepFace Alignment: Substep 2
3D feature point extraction
3D alignment: piecewise affine transformation
No perspective correction
Reference 3D
Fiducial Point Location
Detected 2D
min 𝑟 𝑇 Σ −1 𝑟
𝑟= 𝑥 2𝐷 − 𝑥 3𝐷 𝑃
Final Alignment

12. Motivation
Overview of Methods
Face Detection
Face Alignment
Face Representation
Face Classifier
Results

13. Global Feature: The EigenFace
The set of images A
The dictionary D
The representation W
𝑎𝑟𝑔𝑚𝑖𝑛 𝐷,𝑊 𝐷 𝑛×𝑘 𝑊 𝑘×𝑝 − 𝐴 𝑛×𝑝 𝐹
𝐴𝐴 𝑇 𝐷=𝐷Σ an eigen problem
[Turk 1991]

14. Global Feature: Dictionary Learning
I don’t want negative features: Nonnegative Matrix Factorization
𝑎𝑟𝑔𝑚𝑖𝑛 𝐷,𝑊 𝐷 𝑛×𝑘 𝑊 𝑘×𝑝 − 𝐴 𝑛×𝑝 𝐹
𝑠.𝑡 𝐷≥0
I want less non-zero elements:
Compressed Sensing
𝑎𝑟𝑔𝑚𝑖𝑛 𝐷,𝑊 𝐷 𝑛×𝑘 𝑊 𝑘×𝑝 − 𝐴 𝑛×𝑝 𝐹 +𝜆 𝐷 0
𝑠.𝑡 𝐷≥0

15. Local Features [I Atanasova 2010]
Down Sample
Local Binary Pattern
Laplacian
SIFT
Pros: easy, fast to compute
Cons: not expressive enough

16. The DeepFace [Yaniv Taigman 2014]
Convolution+
Rectified Linear
Convolution+
Rectified Linear
Fully Connected
Max pooling
Locally Connected+
Rectified Linear

17. Motivation
Overview of Methods
Face Detection
Face Alignment
Face Representation
Face Classifier
Results

18. Classifier
Same Person Task:
𝑓 1 − 𝑓 2 Σ
𝑓 1
𝑓 2
Metric Learning: SVM

19. Classifier
Name of the Person Task:

20. Motivation
Overview of Methods
Face Detection
Face Alignment
Face Representation
Face Classifier
Results

21. The Biggest Dataset Ever
The SFC Dataset
From Facebook
each, 4030 people, 4.4M in all
The LFW Dataset
13323 photos of 5749 celebrities

22. The Necessity of Deep Neural Net
More samples, less error
Shallower Neural Net, more error
Small error increase in bigger data set

23. Comparison No alignment: 87.9% Only 2D alignment: 94.3%
Full alignment + DeepFace: >97%

24. Still Challenging On YTF dataset, from Youtube videos
Due to motion blur, view angles

25. Thank You!