[link]
Summary by Alexander Jung 7 years ago
They describe a CNN architecture that can be used to identify a person given an image of their face.
### How
* The expected input is the image of a face (i.e. it does not search for faces in images, the faces already have to be extracted by a different method).
* *Face alignment / Frontalization*
* Target of this step: Get rid of variations within the face images, so that every face seems to look straight into the camera ("frontalized").
* 2D alignment
* They search for landmarks (fiducial points) on the face.
* They use SVRs (features: LBPs) for that.
* After every application of the SVR, the localized landmarks are used to transform/normalize the face. Then the SVR is applied again. By doing this, the locations of the landmarks are gradually refined.
* They use the detected landmarks to normalize the face images (via scaling, rotation and translation).
* 3D alignment
* The 2D alignment allows to normalize variations within the 2D-plane, not out-of-plane variations (e.g. seeing that face from its left/right side). To normalize out-of-plane variations they need a 3D transformation.
* They detect an additional 67 landmarks on the faces (again via SVRs).
* They construct a human face mesh from a dataset (USF Human-ID).
* They map the 67 landmarks to that mesh.
* They then use some more complicated steps to recover the frontalized face image.
* *CNN architecture*
* The CNN receives the frontalized face images (152x152, RGB).
* It then applies the following steps:
* Convolution, 32 filters, 11x11, ReLU (-> 32x142x142, CxHxW)
* Max pooling over 3x3, stride 2 (-> 32x71x71)
* Convolution, 16 filters, 9x9, ReLU (-> 16x63x63)
* Local Convolution, 16 filters, 9x9, ReLU (-> 16x55x55)
* Local Convolution, 16 filters, 7x7, ReLU (-> 16x25x25)
* Local Convolution, 16 filters, 5x5, ReLU (-> 16x21x21)
* Fully Connected, 4096, ReLU
* Fully Connected, 4030, Softmax
* Local Convolutions use a different set of learned weights at every "pixel" (while a normal convolution uses the same set of weights at all locations).
* They can afford to use local convolutions because of their frontalization, which roughly forces specific landmarks to be at specific locations.
* They use dropout (apparently only after the first fully connected layer).
* They normalize "the features" (probably the 4096 fully connected layer). Each component is divided by its maximum value across a training set. Additionally, the whole vector is L2-normalized. The goal of this step is to make the network less sensitive to illumination changes.
* The whole network has about 120 million parameters.
* Visualization of the architecture:
* 
* *Training*
* The network receives images, each showing a face, and is trained to classify the identity of the face (e.g. gets image of Obama, has to return "that's Obama").
* They use cross-entropy as their loss.
* *Face verification*
* In order to tell whether two images of faces show the same person they try three different methods.
* Each of these relies on the vector extracted by the first fully connected layer in the network (4096d).
* Let these vectors be `f1` (image 1) and `f2` (image 2). The methods are then:
1. Inner product between `f1` and `f2`. The classification (same person/not same person) is then done by a simple threshold.
2. Weighted X^2 (chi-squared) distance. Equation, per vector component i: `weight_i (f1[i] - f2[i])^2 / (f1[i] + f2[i])`. The vector is then fed into an SVM.
3. Siamese network. Means here simply that the absolute distance between `f1` and `f2` is calculated (`|f1-f2|`), each component is weighted by a learned weight and then the sum of the components is calculated. If the result is above a threshold, the faces are considered to show the same person.
### Results
* They train their network on the Social Face Classification (SFC) dataset. That seems to be a Facebook-internal dataset (i.e. not public) with 4.4 million faces of 4k people.
* When applied to the LFW dataset:
* Face recognition ("which person is shown in the image") (apparently they retrained the whole model on LFW for this task?):
* Simple SVM with LBP (i.e. not their network): 91.4% mean accuracy.
* Their model, with frontalization, with 2d alignment: ??? no value.
* Their model, no frontalization (only 2d alignment): 94.3% mean accuracy.
* Their model, no frontalization, no 2d alignment: 87.9% mean accuracy.
* Face verification (two images -> same/not same person) (apparently also trained on LFW? unclear):
* Method 1 (inner product + threshold): 95.92% mean accuracy.
* Method 2 (X^2 vector + SVM): 97.00% mean accurracy.
* Method 3 (siamese): Apparently 96.17% accuracy alone, and 97.25% when used in an ensemble with other methods (under special training schedule using SFC dataset).
* When applied to the YTF dataset (YouTube video frames):
* 92.5% accuracy via X^2-method.
