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--- |
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title: Facial Anti-Spoofing Detection |
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emoji: 🐢 |
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colorFrom: pink |
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colorTo: purple |
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sdk: gradio |
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sdk_version: 3.21.0 |
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app_file: app.py |
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pinned: false |
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--- |
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# Face Anti-Spoofing using Deep-Pixel-wise-Binary-Supervision |
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- 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 |
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- 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 |
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- The Trained weights are already saved up as './DeePixBiS.pth' file which can be run on the model. |
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- Training Data has been taken from the NUAA Imposter dataset (863 images subset) |
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### Deep Pixel-wise Binary Supervision |
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This framework uses CNN and densely connected neural network trained using both binary and pixel-wise binary supervision simultaneously. |
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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. |
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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. |
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The model architecture uses the first 8 layers of the DenseNet-161 architecture, for feature extraction. |
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### About the Project |
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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. |
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The Loss function is a weighted sum using the binary and pixel-wise binary cross-entropy loss function. |
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### Requirements |
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- Python 3.6+ |
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- OpenCV |
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- Numpy |
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- PyTorch |
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### Training the Model |
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1. Run `python Train.py` |
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2. After Training is complete the program will generate the file "./DeePixBiS.pth", containing weights of the model |
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### Recognizing |
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1. Run `python Test.py` |
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### TODO |
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1. Make directories for easy handling of python files. |
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2. Add a config file for easy hyperparameters tuning. |
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