Face Recognition Monday, February 1, 2016

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

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?

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

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

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

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

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

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

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

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

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

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]

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

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

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

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

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

Classifier Name of the Person Task:

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

The Biggest Dataset Ever The SFC Dataset From Facebook 800-1200 each, 4030 people, 4.4M in all The LFW Dataset 13323 photos of 5749 celebrities

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

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

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

Thank You!