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  • Mo Guo

    For CS519

    Face Recognition with

    Deep Learning

  • Outline 1. Introduction

    2. Related works

    3. DeepFace

    4. Alignment

    5. Learning

    6. Training

    7. Results

  • • Classical Face recognition pipeline

    Introduction

  • • Shallow Learning

    SIFT, LBP, HOG, etc. Features = handpicked Works well on small datasets but fails on large datasets Also ails on illumination variations and facial expressions

    Face patterns lie on a complex nonlinear and non‐convex manifold in the high‐dimensional space.

    Related Works

  • • Deep Learning Convolutional Neural Networks (CNNs) DeepFace DeepID Series Facenet VGGFace etc.

    Related Works

  • DeepFace (Taigman and Wolf 2014)

    2D/3D face modeling and alignment

    using affine transformations

    9 layer deep neural network

    120 million parameters

  • Alignment(Frontalization) (a) The detected face, with 6 initial fidu- cial points.

    (b) The induced 2D-aligned crop.

    (c) 67 fiducial points on the 2D-aligned crop with their

    corresponding Delaunay triangulation, we added

    triangles on the contour to avoid discontinuities.

    (d) The reference 3D shape transformed to the 2D-

    aligned crop image-plane.

    (e) Triangle visibility w.r.t. to the fitted 3D-2D camera;

    darker triangles are less visible.

    (f) The 67 fiducial points induced by the 3D model that

    are used to direct the piece-wise affine warping.

    (g) The final frontalized crop.

    (h) A new view generated by the 3D model.

  • Deep Learning

    • Input: 3D aligned 3 channel (RGB) face image

    152x152 pixels

    • 9 layer deep neural network architecture

    • Performs softmax for minimizing cross entropy

    loss

    • Uses SGD(stochastic), Dropout, ReLU

    • Outputs k-Class prediction

  • Architecture

    Layer 1-3 :

    • Convolution layers - extract low-level features (e.g. simple edges and

    texture)

    • ReLU after each conv. layer, making the whole cascade produce

    highly non-linear and sparse features

    • Max-pooling: make convolution network more robust to local

    translations.

  • Architecture

    Layer 4-6:

    • Apply filters to different locations on the feature map

    • Similar to a conv. layer but spatially dependent

  • Architecture

    Layer F7

    • Fully connected and generates 4096d vector

    • Sparse representation of face descriptor

    • 75% of outputs are zero

    • mainly due to ReLU and Dropout

  • Architecture

    • F8 calculates probability with softmax

    • softmax produces a distribution over the class labels

    • Cross-entropy loss function: for each training sample

    • Computed using SGD and performs backpropagation

    Layer F8

    • Fully connected and generates 4030d vector

  • Training

    • Trained on SFC 4M faces (4030 identities, 800-1200 images per person)

    • Focus on Labeled Faces in the Wild (LFW) evaluation

    • Used SGD with momentum of 0.9

    • Learning rate 0.01 with manual decreasing, final rate was 0.0001

    • Random weight initializing

    • 15 epochs of training

    • 3 days total on a GPU-based engine

  • Training on SFC

    • Experimented with different depths of networks

    • Removed C3/L4,L5/C3, L4, L5

    • Compared error rate to number of classes K

    – Deeper is better

  • Result on LFW

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