DeepFace: Closing the Gap to Human-Level Performance in Face Verification DeepFace: Closing the Gap to Human-Level Performance in Face Verification
Paper summary ## General stuff about face recognition Face recognition has 4 main tasks: * **Face detection**: Given an image, draw a rectangle around every face * **Face alignment**: Transform a face to be in a canonical pose * **Face representation**: Find a representation of a face which is suitable for follow-up tasks (small size, computationally cheap to compare, invariant to irrelevant changes) * **Face verification**: Images of two faces are given. Decide if it is the same person or not. The face verification task is sometimes (more simply) a face classification task (given a face, decide which of a fixed set of people it is). Datasets being used are: * **LFW** (Labeled Faces in the Wild): 97.35% accuracy; 13 323 web photos of 5 749 celebrities * **YTF** (YouTube Faces): 3425 YouTube videos of 1 595 subjects * **SFC** (Social Face Classification): 4.4 million labeled faces from 4030 people, each 800 to 1200 faces * **USF** (Human-ID database): 3D scans of faces ## Ideas in this paper This paper deals with face alignment and face representation. **Face Alignment** They made an average face with the USF dataset. Then, for each new face, they apply the following procedure: * Find 6 points in a face (2 eyes, 1 nose tip, 2 corners of the lip, 1 middle point of the bottom lip) * Crop according to those * Find 67 points in the face / apply them to a normalized 3D model of a face * Transform (=align) face to a normalized position **Representation** Train a neural network on 152x152 images of faces to classify 4030 celebrities. Remove the softmax output layer and use the output of the second-last layer as the transformed representation. The network is: * C1 (convolution): 32 filters of size $11 \times 11 \times 3$ (RGB-channels) (returns $142\times 142$ "images") * M2 (max pooling): $3 \times 3$, stride of 2 (returns $71\times 71$ "images") * C3 (convolution): 16 filters of size $9 \times 9 \times 16$ (returns $63\times 63$ "images") * L4 (locally connected): $16\times9\times9\times16$ (returns $55\times 55$ "images") * L5 (locally connected): $16\times7\times7\times16$ (returns $25\times 25$ "images") * L6 (locally connected): $16\times5\times5\times16$ (returns $21\times 21$ "images") * F7 (fully connected): ReLU, 4096 units * F8 (fully connected): softmax layer with 4030 output neurons The training was done with: * Stochastic Gradient Descent (SGD) * Momentum of 0.9 * Performance scheduling (LR starting at 0.01, ending at 0.0001) * Weight initialization: $w \sim \mathcal{N}(\mu=0, \sigma=0.01)$, $b = 0.5$ * ~15 epochs ($\approx$ 3 days) of training ## Evaluation results * **Quality**: * 97.35% accuracy (or mean accuracy?) with an Ensemble of DNNs for LFW * 91.4% accuracy with a single network on YTF * **Speed**: DeepFace runs in 0.33 seconds per image (I'm not sure which size). This includes image decoding, face detection and alignment, **the** feed forward network (why only one? wasn't this the best performing Ensemble?) and final classification output

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