Face Anti-Spoofing using Deep-Pixel-wise-Binary-Supervision

• Anti-Spoofing for Face Recognition task using the Deep Pixel-wise Binary Supervision Technique. The paper can be found here https://arxiv.org/pdf/1907.04047v1.pdf
• This Project implements the DeePixBiS model using Python OpenCV, and the Pytorch Framework. This project is inspired from https://github.com/voqtuyen/deep-pix-bis-pad.pytorch
• The Trained weights are already saved up as './DeePixBiS.pth' file which can be run on the model.
• Training Data has been taken from the NUAA Imposter dataset (863 images subset)

Deep Pixel-wise Binary Supervision

This framework uses CNN and densely connected neural network trained using both binary and pixel-wise binary supervision simultaneously. This is a frame level algorithm, which performs the task individually and independently on each frame, thus making computation and time feasable for practical use. Each pixel/patch of the frame is given a binary label depending on whether it is bonafide or an attack, trying to generate the depth-map of the image. Note that this framework does not generate a precise depth map, rather it does not need to. In the testing phase, the mean of this feature map is used as the score. If the score is greater than a threshold value, it is declared to be real. The model architecture uses the first 8 layers of the DenseNet-161 architecture, for feature extraction.

About the Project

We use the OpenCV library for the image preproccsing for the model. OpenCV offers several cascades for the task of object Detection. We use the Frontal-Face Haar Cascade to detect a "face" in the frame. Once a face is detected it has a bounded box to find its location, and the face is extracted, leaving aside the other non-important details in the frame. The training-data(frames) ready to pass through the model is trained using the Adam Optimizer. The Loss function is a weighted sum using the binary and pixel-wise binary cross-entropy loss function.

Requirements

• Python 3.6+
• OpenCV
• Numpy
• PyTorch

Training the Model

1. Run python Train.py
2. After Training is complete the program will generate the file "./DeePixBiS.pth", containing weights of the model

Recognizing

1. Run python Test.py

TODO

1. Make directories for easy handling of python files.
2. Add a config file for easy hyperparameters tuning.