300w Dataset Download WORK

Automatic facial landmark detection is a longstanding problem in computer vision, and 300-W Challenge is the first event of its kind organized exclusively to benchmark the efforts in the field. The particular focus is on facial landmark detection in real-world datasets of facial images captured in-the-wild. The results of the Challenge will be presented at the 300-W Faces in-the-Wild Workshop to be held in conjunction with ICCV 2013.

300-W is an image dataset that focuses on face images. It contains 300 indoor images and 300 outdoor images with human faces, captured in the wild. It covers a variety of identities, facial expressions, lighting conditions, poses, occlusions, and face sizes.

300w Dataset Download

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In a significant development, two extended versions of the original 300W dataset were introduced, called 300W-LP and 300W-LPA. What is special about these datasets is that they started from the same 300W images, but used a generative technique to create multiple variations of each face images, with automatically generated annotations.

The 300-W Challenge was held in the years 2013 and 2014. It was the first time an event was organized specifically to benchmark the automated facial landmark recognition efforts. It focused on identifying facial landmarks in real-world face image datasets.

The LFPW, AFW, HELEN, and XM2VTS datasets were re-annotated with the markup shown in the following image. There were also annotations for an additional 135 images of faces with different expressions and poses (the iBUG training set).

Participants in the 300-W challenge tested their algorithms on the 300-W test dataset. Submissions were evaluated using standard bounding-box initialization when running each trained algorithm on the 300-W test dataset. Performance was evaluated based on the 68-point markup in the above image, and 51 points corresponding to the borderless points.

Although there are comprehensive benchmarks for locating facial landmarks in still images, efforts to benchmark facial landmarks in video are very limited. 300 Videos in the Wild (300-VW) is a dataset for evaluating landmark tracking algorithms on videos containing faces. It contains 114 videos.

Authors of the dataset collected videos of faces recorded in the wild. Each video is approximately 1 minute (25-30 fps) in length. All frames are annotated with the same 68 facial landmarks used in the 300W dataset.

The 300W-LPA (Large Pose Augmented) dataset builds on 300W-LP, augmenting it with more data by rotating each image in pitch, 5 degrees at a time, up to 25 degrees in both directions. The dataset contains 366,564 photos of 59,439 individuals.

One approach to perform this type of transformation is active appearance models (AAM). However, these models are difficult to fit and do not generalize well to unseen images. The authors showed that active orientation models (AOMs) can provide much better performance. They proposed a tool based on AOM which performs semi-automatic annotation for variations of existing face images. This tool was the basis for the extended LP/LPA datasets.

Deep Lake users may have access to a variety of publicly available datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have a license to use the datasets. It is your responsibility to determine whether you have permission to use the datasets under their license.

You can load a 300w dataset fast with one line of code using the open-source package Activeloop Deep Lake in Python. See detailed instructions on how to load a 300w dataset training subset in Python.

You can stream a 300w dataset while training a model in PyTorch or TensorFlow with one line of code using the open-source package Activeloop Deep Lake in Python. See detailed instructions on how to train a model on a 300w dataset with PyTorch in Python or train a model on a 300w dataset with TensorFlow in Python.

While dlib landmark detector almost fully satisfies my needs (except that I would also like to have eye pupils marked), however, dlib's facial landmark detection algorithm was trained on iBug-300W dataset, and that is not meant for commercial use.

Instead, we have trained a custom dlib shape predictor that only localizes the eye regions. (i.e., our model is not trained on the other facial structures in the iBUG-300W dataset including i.e., eyebrows, nose, mouth, and jawline).

To train our shape predictor we utilized the iBUG-300W dataset, only instead of training our model to recognize all facial structures (i.e., eyes, eyebrows, nose, mouth, and jawline), we instead trained the model to localize just the eyes.

We present TED-Face, a new method for recovering high-fidelity 3D facial geometry and appearance with enhanced textures from single-view images. While vision-based face reconstruction has received intensive research in the past decades due to its broad applications, it remains a challenging problem because human eyes are particularly sensitive to numerically minute yet perceptually significant details. Previous methods that seek to minimize reconstruction errors within a low-dimensional face space can suffer from this issue and generate close yet low-fidelity approximations. The loss of high-frequency texture details is a key factor in their process, which we propose to address by learning to recover both dense radiance residuals and sparse facial texture features from a single image, in addition to the variables solved by previous work-shape, appearance, illumination, and camera. We integrate the estimation of all these factors in a single unified deep neural network and train it on several popular face reconstruction datasets. We also introduce two new metrics, visual fidelity (VIF) and structural similarity (SSIM), to compensate for the fact that reconstruction error is not a consistent perceptual metric of quality. On the popular FaceWarehouse facial reconstruction benchmark, our proposed system achieves a VIF score of 0.4802 and an SSIM score of 0.9622, improving over the state-of-the-art Deep3D method by 6.69% and 0.86%, respectively. On the widely used LS3D-300W dataset, we obtain a VIF score of 0.3922 and an SSIM score of 0.9079 for indoor images, and the scores for outdoor images are 0.4100 and 0.9160, respectively, which also represent an improvement over those of Deep3D. These results show that our method is able to recover visually more realistic facial appearance details compared with previous methods.

Concretely, we formalize the concept of mesh-tension and use it to aggregate possible wrinkles from high-quality expression scans into albedo and displacement texture maps. At synthesis, we use these maps to produce wrinkles even for expressions not represented in the source scans. Additionally, to provide a more nuanced indicator of model performance under deformations resulting from compressed expressions, we introduce the 300W-winks evaluation subset and the Pexels dataset of closed eyes and winks.

This paper investigates how far a very deep neural network is from attaining close to saturating performance on existing 2D and 3D face alignment datasets. To this end, we make the following three contributions: (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and finally evaluate it on all other 2D facial landmark datasets. (b) We create a guided by 2D landmarks network which converts 2D landmark annotations to 3D and unifies all existing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date (~230,000 images). (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W. (d) We further look into the effect of all "traditional" factors affecting face alignment performance like large pose, initialization and resolution, and introduce a "new" one, namely the size of the network. (e) We show that both 2D and 3D face alignment networks achieve performance of remarkable accuracy which is probably close to saturating the datasets used.

LS3D-W is a large-scale 3D face alignment dataset constructed by annotating the images from AFLW[2], 300VW[3], 300W[4] and FDDB[5] in a consistent manner with 68 points using the automatic method described in [1].

Update: The entire LS3D-W dataset has now been released. In addition, we also made available the pretrained 2D-to-3D-FAN model to allow conversion of existing 2D points to 3D (the 2D points must be annotated in a consistent manner with the training set used).

References:

[1] A. Bulat, G. Tzimiropoulos. How far are we from solving the 2D \& 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks), arxiv, 2017

[2] M. Kostinger, P. Wohlhart, P.M. Roth, and H. Bischof. Annotated facial landmarks in the wild: A large-scale, real-world database for facial landmark localization, In ICCVW, 2011

[3] J. Shen, S. Zafeiriou, G. G. Chrysos, J. Kossaifi, G. Tzimiropoulos, and M. Pantic. The first facial landmark tracking in-the-wild challenge: Benchmark and results. In ICCVW, 2015

[4] C. Sagonas, G. Tzimiropoulos, S. Zafeiriou, and M. Pantic. 300 faces in-the-wild challenge: The first facial landmark localization challenge. In ICCVW, 2013

[5] V. Jain, E. Learned-Miller FDDB: A Benchmark for Face Detection in Unconstrained Settings. UMass Amherst Technical Report, 2010

In this article, we present the Menpo 2D and Menpo 3D benchmarks, two new datasets for multi-pose 2D and 3D facial landmark localisation and tracking. In contrast to the previous benchmarks such as 300W and 300VW, the proposed benchmarks contain facial images in both semi-frontal and profile pose. We introduce an elaborate semi-automatic methodology for providing high-quality annotations for both the Menpo 2D and Menpo 3D benchmarks. In Menpo 2D benchmark, different visible landmark configurations are designed for semi-frontal and profile faces, thus making the 2D face alignment full-pose. In Menpo 3D benchmark, a united landmark configuration is designed for both semi-frontal and profile faces based on the correspondence with a 3D face model, thus making face alignment not only full-pose but also corresponding to the real-world 3D space. Based on the considerable number of annotated images, we organised Menpo 2D Challenge and Menpo 3D Challenge for face alignment under large pose variations in conjunction with CVPR 2017 and ICCV 2017, respectively. The results of these challenges demonstrate that recent deep learning architectures, when trained with the abundant data, lead to excellent results. We also provide a very simple, yet effective solution, named Cascade Multi-view Hourglass Model, to 2D and 3D face alignment. In our method, we take advantage of all 2D and 3D facial landmark annotations in a joint way. We not only capitalise on the correspondences between the semi-frontal and profile 2D facial landmarks but also employ joint supervision from both 2D and 3D facial landmarks. Finally, we discuss future directions on the topic of face alignment. 17dc91bb1f