人脸识别+数据采集+人脸特征提取（哈哈哈，绝决子） dlib库，shape全部代码

# dlib-models This repository contains trained models created by me (Davis King). They are provided as part of the dlib example programs, which are intended to be educational documents that explain how to use various parts of the dlib library. As far as I am concerned, anyone can do whatever they want with these model files as I've released them into the public domain. Details describing how each model was created are summarized below. ## dlib_face_recognition_resnet_model_v1.dat.bz2 This model is a ResNet network with 29 conv layers. It's essentially a version of the ResNet-34 network from the paper Deep Residual Learning for Image Recognition by He, Zhang, Ren, and Sun with a few layers removed and the number of filters per layer reduced by half. The network was trained from scratch on a dataset of about 3 million faces. This dataset is derived from a number of datasets. The face scrub dataset (http://vintage.winklerbros.net/facescrub.html), the VGG dataset (http://www.robots.ox.ac.uk/~vgg/data/vgg_face/), and then a large number of images I scraped from the internet. I tried as best I could to clean up the dataset by removing labeling errors, which meant filtering out a lot of stuff from VGG. I did this by repeatedly training a face recognition CNN and then using graph clustering methods and a lot of manual review to clean up the dataset. In the end about half the images are from VGG and face scrub. Also, the total number of individual identities in the dataset is 7485. I made sure to avoid overlap with identities in LFW. The network training started with randomly initialized weights and used a structured metric loss that tries to project all the identities into non-overlapping balls of radius 0.6. The loss is basically a type of pair-wise hinge loss that runs over all pairs in a mini-batch and includes hard-negative mining at the mini-batch level. The resulting model obtains a mean error of 0.993833 with a standard deviation of 0.00272732 on the LFW benchmark. ## mmod_dog_hipsterizer.dat.bz2 This dataset is trained on the data from the Columbia Dogs dataset, which was introduced in the paper: Dog Breed Classification Using Part Localization Jiongxin Liu, Angjoo Kanazawa, Peter Belhumeur, David W. Jacobs European Conference on Computer Vision (ECCV), Oct. 2012. The original dataset is not fully annotated. So I created a new fully annotated version which is available here: http://dlib.net/files/data/CU_dogs_fully_labeled.tar.gz ## mmod_human_face_detector.dat.bz2 This is trained on this dataset: http://dlib.net/files/data/dlib_face_detection_dataset-2016-09-30.tar.gz. I created the dataset by finding face images in many publicly available image datasets (excluding the FDDB dataset). In particular, there are images from ImageNet, AFLW, Pascal VOC, the VGG dataset, WIDER, and face scrub. All the annotations in the dataset were created by me using dlib's imglab tool. ## resnet34_1000_imagenet_classifier.dnn.bz2 This is trained on the venerable ImageNet dataset. ## shape_predictor_5_face_landmarks.dat.bz2 This is a 5 point landmarking model which identifies the corners of the eyes and bottom of the nose. It is trained on the [dlib 5-point face landmark dataset](http://dlib.net/files/data/dlib_faces_5points.tar), which consists of 7198 faces. I created this dataset by downloading images from the internet and annotating them with dlib's imglab tool. The exact program that produced the model file can be found [here](https://github.com/davisking/dlib/blob/master/tools/archive/train_face_5point_model.cpp). This model is designed to work well with dlib's HOG face detector and the CNN face detector (the one in mmod_human_face_detector.dat). ## shape_predictor_68_face_landmarks.dat.bz2 This is trained on the ibug 300-W dataset (https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/) C. Sagonas, E. Antonakos, G, Tzimiropoulos, S. Zafeiriou, M. Pantic. 300 faces In-the-wild challenge: Database and results. Image and Vision Computing (IMAVIS), Special Issue on Facial Landmark Localisation "In-The-Wild". 2016. The license for this dataset excludes commercial use and Stefanos Zafeiriou, one of the creators of the dataset, asked me to include a note here saying that the trained model therefore can't be used in a commerical product. So you should contact a lawyer or talk to Imperial College London to find out if it's OK for you to use this model in a commercial product. Also note that this model file is designed for use with dlib's HOG face detector. That is, it expects the bounding boxes from the face detector to be aligned a certain way, the way dlib's HOG face detector does it. It won't work as well when used with a face detector that produces differently aligned boxes, such as the CNN based mmod_human_face_detector.dat face detector. ## shape_predictor_68_face_landmarks_GTX.dat.bz2 The GTX model is the result of applying a set of training strategies and implementation optimization described in: Alvarez Casado, C., Bordallo Lopez, M. Real-time face alignment: evaluation methods, training strategies and implementation optimization. Springer Journal of Real-time image processing, 2021 The resulted model is smaller, faster, smoother and more accurate. You can find all the details related to the training and testing in the next Gitlab repository: https://gitlab.com/visualhealth/vhpapers/real-time-facealignment This is trained on the ibug 300-W dataset (https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/) C. Sagonas, E. Antonakos, G, Tzimiropoulos, S. Zafeiriou, M. Pantic. 300 faces In-the-wild challenge: Database and results. Image and Vision Computing (IMAVIS), Special Issue on Facial Landmark Localisation "In-The-Wild". 2016. The license for this dataset excludes commercial use and Stefanos Zafeiriou, one of the creators of the dataset, asked me to include a note here saying that the trained model therefore can't be used in a commerical product. So you should contact a lawyer or talk to Imperial College London to find out if it's OK for you to use this model in a commercial product. Also note that this model file with increased robustness to face detectors. However, it works best when the bounding boxes are squared, as it is que case with both dlib's HOG face detector or the CNN based mmod_human_face_detector.dat face detector. It won't work as well when used with other face detectors that produce rectangular boxes. ## mmod_rear_end_vehicle_detector.dat.bz2 This model is trained on the [dlib rear end vehicles dataset](http://dlib.net/files/data/dlib_rear_end_vehicles_v1.tar). The dataset contains images from vehicle dashcams which I manually annotated using dlib's imglab tool. ## mmod_front_and_rear_end_vehicle_detector.dat.bz2 This model is trained on the [dlib front and rear end vehicles dataset](http://dlib.net/files/data/dlib_front_and_rear_vehicles_v1.tar). The dataset contains images from vehicle dashcams which I manually annotated using dlib's imglab tool. ## dnn_gender_classifier_v1.dat.bz2 This model is a gender classifier trained using a private dataset of about 200k different face images and was generated according to the network definition and settings given in [Minimalistic CNN-based ensemble model for gender prediction from face images](http://www.eurecom.fr/fr/publication/4768/download/mm-publi-4768.pdf). Even if the dataset used for the training is different from that used by G. Antipov et al, the classification results on the LFW evaluation are similar overall (± 97.3%). To take up the authors' proposal to join the results of three networks, a simplification was made by finally presenting RGB images, thus simulating three "grayscale" networks via the three image planes. Better results could be probably obtained with a more complex and deeper network, bu