一些处理人脸数据集的脚本。_MATLAB_Python_下载.zip

# Matlab codes for evaluation on MegaFace Requirement: 1. [MatMTCNN](https://github.com/happynear/MTCNN_face_detection_alignment/tree/master/code/codes/vs/MatCascadeFaceDetection). If you are not using Windows, you need to modify some of the codes to directly use the [Matlab version of MTCNN](https://github.com/kpzhang93/MTCNN_face_detection_alignment). If you have done such a work, I'm glad to merge your codes. 2. [Megaface](http://megaface.cs.washington.edu/participate/challenge.html). Please download `Our dataset`, `FaceScrub full tgz`, `FaceScrub bounding boxes actors txt`, `FaceScrub bounding boxes actresses txt`, `Linux Development Kit`. Procedure: 1. Align Facescrub: `align_facescrub.m`. Then mannully confirm some failed samples through `align_facescrub_failures.m`. 2. Align Megaface: `align_megaface_from_list.m`. Then align failed samples through `align_megaface_failures.m`. 3. Extract Features using `extract_facescrub_feature.m`, `extract_megaface_feature.m`. Alignment Logic: The provided 3-point labels are the most accurate. The second accurate information is the bounding box. All 5-point labels are totally wrong. So our logic is as follows. 1. `align_megaface_from_list.m`: If there is 3-point label, rotate and crop the image according to it. Then detect and align face from the cropped image. 2. `align_megaface_failures.m`: If there is no 3-point label or failed to detect from the cropped image, detect face from the raw image.         If the detected face and the given bounding box's IoU is over 30%, align this face.         If the IoU is below 30%, use the last two networks of MTCNN to forcely get the face score and 5 keypoints from the cropped image based on the given bounding box.         If the face score is above 0.3, use the detected 5 points to align the face.         If all methods are failed, directly crop the middle area of a face as the aligned face.