If your application performs landmark detection, face alignment, face recognition or face analysis, the first step is always face detection. And in fact, face detection progressed tremendously in the last few years. The existing algorithms successfully address such challenges as large variations in scale, pose or appearance.Datasets : How to Download?
However, there are still some issues that are not specifically captured by the existing approaches and face detection datasets. The group of researchers, headed by Hajime Nada from Fujitsu, identified a new set of challenges for face detection and even collected a dataset of face images that involved these issues.
In particular, their dataset includes images with rain, snow, haze, illumination variations, motion and focus blur, and lens impediments. Is there a gap between their performance and real-world requirements? Several datasets have been created specifically for face detection. The table below summarizes information on the most widely used datasets.
As you can see, even though there are some huge datasets with large variations in face appearance, there is still a lack of datasets that capture weather-based degradations and other challenging conditions with a large set of images in each condition.
It captures variations in weather conditions rain, snow, hazemotion and focus blur, illumination variations, lens impediments. See the distribution of images in the table below.
Notably, the UFDD dataset also includes a large set of distractor images that is usually ignored by the existing datasets. Distractors either contain non-human faces such as animal faces or no faces at all.
The presence of such images is especially important to measure the performance of a face detector in rejecting non-face images and to study the false positive rate of the algorithms. After collection and duplicates removal, the images were resized to have a width of while preserving their original aspect ratio. For annotations, the images were uploaded to Amazon mechanical turk AMT. Each image was assigned to around 5 to 9 AMT workers, who were asked to annotate all recognizable faces in the image.
Once the annotation was complete, the labels were cleaned and consolidated. The researchers selected several recent face detection approaches to evaluate them on the proposed UFDD dataset:.
The next figure shows the precision-recall curves corresponding to different approaches as evaluated on the UFDD dataset. Table 3 below contains the mean average precision mAP corresponding to different methods and different training sets. As you can see, these new challenging conditions are not well-addressed by the existing state-of-the-art approaches. However, the detection performance improves when the networks are trained on the synthesized dataset. This further confirms the necessity of the dataset that reflects the real-world conditions such as rain and haze.
Next, the researchers individually analyzed the effect of different conditions on the performance of recent state-of-the-art face detection methods. See below the detections results for all benchmark methods:. The results demonstrate that all the degradations hinder the performance of the benchmarked methods.
Evaluation results also uncover a significant effect of the distractors on the performance of our face detection algorithms.It contains over 4, color images corresponding to people's faces 70 men and 56 women. Images feature frontal view faces with different facial expressions, illumination conditions, and occlusions sun glasses and scarf. The pictures were taken at the CVC under strictly controlled conditions. No restrictions on wear clothes, glasses, etc.
Each person participated in two sessions, separated by two weeks 14 days time. The same pictures were taken in both sessions.
This face database is publicly available and can be obtained from this web-site. It is free for academic use.
Commercial distribution or any act related to commercial use of this database is strictly prohibited. See a movie example due to compression the quality of this video is not very good : arfd2. Images are of by pixels and of 24 bits of depth. A total of 30 sequences of images were also grabbed to test dynamic systems. Each sequence consist of 25 color images same size as above. You can convert images from RAW to any other format using ImageMagick using convert or any other image software.
This is only an example of how the images of the AR face database look like. Images have been reduced in size and except the first one all images have been converted to greyscale images 8 bits and saved as JPG with 75 quality rate.
To get a real example click here. This database is publicly available. It is free for professors and researcher scientists affiliated to a University. All publications and works that use the AR face database must reference the following report: A.
Martinez and R.
The AR Face Database. Permission to use but not reproduce or distribute the AR face database is granted to all researchers given that the following steps are properly followed:.
Send an e-mail to Prof. Aleix M. Martinez before downloading the database. You will need a user-name and password to access the files of this database. I have read and agree to the terms and conditions specified in the AR face database webpage. This database will only be used for research purposes. I will not make any part of this database available to a third party.
I'll not sell any part of this database or make any profit from its use. All submitted papers or any publicly available text that uses or talks about the AR face database must cite the following report: A. Report 24, Permission is NOT granted to reproduce the database or posted itinto any webpage that is not the AR face database web-page administreted by Prof.
Written permission must be obtained from Prof. Even then, the database cannot be posted on a web-page accessible from outside the faculty research group.We choose 32, images and labelfaces with a high degree of variability in scale, pose and occlusion as depicted in the sample images. Similar to MALF and Caltech datasets, we do not release bounding box ground truth for the test images. Users are required to submit final prediction files, which we shall proceed to evaluate.
For details on the evaluation scheme please refer to the technical report. For detection resutls please refer to the result page. Please contact us to evaluate your detection results.
An evaluation server will be available soon. The detection result for each image should be a text file, with the same name of the image. The detection results are organized by the event categories. For example, if the directory of a testing image is ". The detection output is expected in the follwing format Each text file should contain 1 row per detected bounding box, in the format "[left, top, width, height, score]".
Below we list other face detection datasets. A more detailed comparison of the datasets can be found in the paper. For questions and result submission, please contact Shuo Yang at shuoyang. News The new version of evaluation code and validation results is released. Benchmark For details on the evaluation scheme please refer to the technical report. Submission Please contact us to evaluate your detection results. Related Datasets Below we list other face detection datasets.
IJB-A contains 24, images and 49, faces. MALF consists of 5, images and 11, faces. It has images with labeled faces. For each face, annotations include a rectangular bounding box, 6 landmarks and the pose angles.Sagonas, E. Antonakos, G, Tzimiropoulos, S. Zafeiriou, M. Sagonas, G. Tzimiropoulos, S. Sydney, Australia, December A semi-automatic methodology for facial landmark annotation. Oregon, USA, June Automatic facial landmark detection is a longstanding problem in computer vision, and 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. A special issue of Image and Vision Computing Journal will present the best performing methods and summarize the results of the Challenge.
All participants in the Challenge will be able to train their algorithms using these data. Performance evaluation will be carried out on W test set, using the same Multi-PIE mark-up, and the same face-bounding box initialization.
Figure 1: The 68 and 51 points mark-up used for our annotations. We provide additional annotations for another images in difficult poses and expressions IBUG training set. Annotations have the same name as the corresponding images. All annotations can be downloaded from here. Participants are strongly encouraged to train their algorithms using these training data. Should you use any of the provided annotations please cite  and the paper presenting the corresponding database.
Please note that the re-annotated data for this challenge are saved in the matlab convention of 1 being the first index, i.
Participants will have their algorithms tested on a newly collected data set with 2x indoor and outdoor face images collected in the wild W test set. Sample images are shown in Fig 2 and Fig 3. Participants should send binaries with their trained algorithms to the organisers, who will run each algorithm on the W test set using the same bounding box initialization.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? Sign in to your account. Hi, thank you for your great work. But when I test the models on the AFW dataset, the results are very different. I wrote my own code to compute the discrete predictions that rounds of the the nearest 15 degree, the yaw accuracy is only So even if I made some mistake calculating the discrete predictions, the MAE of yaw seems too large.
I am wondering which step was missing to reproduce the result in the paper. I have make sure that the input format are the same as the one required in the datasets. Hi, can you try using -yaw instead of yaw for evaluation? The format of the afw annotations are different.
EDIT: As said in my final comment. Also the test code will be released and includes specific testing schemes for AFW which might be a problem in your implementation. We will also release the AFLW trained model. Thanks for the quick response. After I inverted the sign of all yaw labels, the accuracy is still only I think that I am missing some part of the implementation details. Will look into the details more carefully while waiting for your testing code.
Thank you in advance! Sorry I still haven't got the chance to upload the testing code but I verified that I obtain the correct paper results.
Give me another week and it'll be up!
WIDER FACE: A Face Detection Benchmark
Hopenet Alpha-1 Yaw: 7. And, training script is not working. The training script is now working. Also, if you look closely you can find the training parameters in the paper.Labeled Faces in the Wild Home. Thanks to all that have participated in making LFW a success! New results page: We have recently updated and changed the format and content of our results page.
Please refer to the new technical report for details of the changes. No matter what the performance of an algorithm on LFW, it should not be used to conclude that an algorithm is suitable for any commercial purpose. There are many reasons for this. Here is a non-exhaustive list: Face verification and other forms of face recognition are very different problems. For example, it is very difficult to extrapolate from performance on verification to performance on 1:N recognition.
Many groups are not well represented in LFW. For example, there are very few children, no babies, very few people over the age of 80, and a relatively small proportion of women.
In addition, many ethnicities have very minor representation or none at all. While theoretically LFW could be used to assess performance for certain subgroups, the database was not designed to have enough data for strong statistical conclusions about subgroups.
Simply put, LFW is not large enough to provide evidence that a particular piece of software has been thoroughly tested. Additional conditions, such as poor lighting, extreme pose, strong occlusions, low resolution, and other important factors do not constitute a major part of LFW. For all of these reasons, we would like to emphasize that LFW was published to help the research community make advances in face verification, not to provide a thorough vetting of commercial algorithms before deployment.
While there are many resources available for assessing face recognition algorithms, such as the Face Recognition Vendor Tests run by the USA National Institute of Standards and Technology NISTthe understanding of how to best test face recognition algorithms for commercial use is a rapidly evolving area. Some of us are actively involved in developing these new standards, and will continue to make them publicly available when they are ready.
Welcome to Labeled Faces in the Wild, a database of face photographs designed for studying the problem of unconstrained face recognition. The data set contains more than 13, images of faces collected from the web. Each face has been labeled with the name of the person pictured. The only constraint on these faces is that they were detected by the Viola-Jones face detector.
More details can be found in the technical report below. There are now four different sets of LFW images including the original and three different types of "aligned" images. Among these, LFW-a and the deep funneled images produce superior results for most face verification algorithms over the original images and over the funneled images ICCV Annotated Facial Landmarks in the Wild AFLW provides a large-scale collection of annotated face images gathered from the web, exhibiting a large variety in appearance e.
In total about 25k faces are annotated with up to 21 landmarks per image. Annotated Facial Landmarks in the Wild AFLW provides a large-scale collection of annotated face images gathered from Flickr, exhibiting a large variety in appearance e. A short comparison to other important face databases with annotated landmarks is provided here:. The motivation for the AFLW database is the need for a large-scale, multi-view, real-world face database with annotated facial features.
We gathered the images on Flickr using a wide range of face relevant tags e. The downloaded set of images was manually scanned for images containing faces. The key data and most important properties of the database are:.
Due to the nature of the database and the comprehensive annotation we think it is well suited to train and test algorithms for. If you agree with the terms of the license agreement contact Michael Opitz michael. Please send the email from your official account so we can verify your affiliation and include your. We want to thank all people who have been involved in the annotation process, especially, the interns at the institute and the colleagues from the Documentation Center of the National Defense Academy of Austria.
Angelova, Y. Abu-Mostafam, and P. Pruning training sets for learning of object categories. In Proc. CVPR, Aran, I. Ari, M. Guvensan, H. Haberdar, Z. Kurt, H. Turkmen, A. Uyar, and L. A database of non-manual signs in turkish sign language. Signal Processing and Communications Applications, Jesorsky, K. Kirchberg, and R.
Robust face detection using the Hausdorff distance. Audio and Video-based Biometric Person Authentication, The PUT face database. Martinez and R. The AR face database. Messer, J. Matas, J.