In this paper, we introduce a victim dataset for the RoboCup Rescue competitions. The RoboCup Rescue robots have to collect points within several disciplines, e.g. a search task within an area to survey simulated baby doll (victim). When a robot comes across a victim, a heat detector does not completely proof if this is a living being and not just a heat emitting somewhat else. Further investigations are necessary so that a face detection could prove the existence of a victim. Lots of face detection approaches can be found in literature, which manly are used for human face recognition. These cannot be straightforward used for victim faces which are, in case of the RoboCup Rescue competitions, typically dolls. Thus we present the results of standard approaches and developed an own approach via bag-of-visual-words (BoVW).

High-resolution thermal infrared face database with extensive manual annotations, introduced by Kopaczka et al, 2018. Useful for training algoeithms for image processing tasks as well as facial expression recognition. The full database itself, all annotations and the complete source code are freely available from the authors for research purposes at https://github.com/marcinkopaczka/thermalfaceproject. Please cite following papers for the dataset: [1] M. Kopaczka, R. Kolk and D. Merhof, "A fully annotated thermal face database and its application for thermal facial expression recognition," 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2018, pp. 1-6, doi: 10.1109/I2MTC.2018.8409768. [2] Kopaczka, M., Kolk, R., Schock, J., Burkhard, F., & Merhof, D. (2018). A thermal infrared face database with facial landmarks and emotion labels. IEEE Transactions on Instrumentation and Measurement, 68(5), 1389-1401.

Unconstrained Face Detection and Open-Set Face Recognition Challenge * Paper: https://arxiv.org/abs/1708.02337 * Official website: https://vast.uccs.edu/Opensetface * UnOfficial website: https://exposing.ai/uccs Face detection and recognition benchmarks have shifted toward more difficult environments. The challenge presented in this paper addresses the next step in the direction of automatic detection and identification of people from outdoor surveillance cameras. While face detection has shown remarkable success in images collected from the web, surveillance cameras include more diverse occlusions, poses, weather conditions and image blur. Although face verification or closed-set face identification have surpassed human capabilities on some datasets, open-set identification is much more complex as it needs to reject both unknown identities and false accepts from the face detector. We show that unconstrained face detection can approach high detection rates albeit with moderate false accept rates. By contrast, open-set face recognition is currently weak and requires much more attention.

This dataset comprises over 26,000 full names annotated with genders. Source: Gender Prediction Based on Vietnamese Names with Machine Learning Techniques

A high-resolution version of VGGFace2 for academic face editing purposes. This project uses GFPGAN for image restoration and insightface for data preprocessing (crop and align).

WildestFaces is tailored to study cross-domain recognition under a variety of adverse conditions. Source: Red Carpet to Fight Club: Partially-supervised Domain Transfer for Face Recognition in Violent Videos

The scales of the data accessible through internet search engines can reach hundreds of millions, or even billions. The existence of such large weak-labeled databases has gained importance in the training of face recognition algorithms. Starting with the publicly available YFCC100M, we propose a weakly-labeled subset for multi-label face recognition for self-supervised methods. A 392K image subset of YFCC100M of 128x128 images was obtained by querying for the 40 facial attributes. We made this dataset publicly available.

Description: 4,458 People - 3D Facial Expressions Recognition Data. The collection scenes include indoor scenes and outdoor scenes. The dataset includes males and females. The age distribution ranges from juvenile to the elderly, the young people and the middle aged are the majorities. The device includes iPhone X, iPhone XR. The data diversity includes different expressions, different ages, different races, different collecting scenes. This data can be used for tasks such as 3D facial expression recognition. Data size: 4,458 people, 7 kinds of 3D expressions were collected for each person Population distribution: race distribution: Asian (Chinese), Black, Caucasian; gender distribution: male, female; age distribution: ranging from teenager to the elderly, the middle-aged and young people are the majorities

Dataset for the DREAMING - Diminished Reality for Emerging Applications in Medicine through Inpainting Challenge! The DREAMING Challenge is part of ISBI 2024. Participate for Awards, Money Prices, Proceedings Paper, Top-Tier Challenge Summary Paper! More information about the challenge and registration can be found on the challenge platform! The DREAMING training dataset contains 100 diverse, photorealistic surgery scenes with unique patients, surroundings and obstructions from dynamically moving and static surgical instruments and hands holding them. We also provide the masks defining the area to be inpainted for each image.

The free Face dataset made for students and teachers. It contains 10,000 photos with equal distribution of race and gender parameters, along with metadata and facial landmarks. Free to use for research with citation Photos by Generated.Photos. Photos All the photos are 100% synthetic. Based on model-released photos. Royalty-free. Can be used for any research purpose except for the ones violating the law. Worldwide. No time limitations. Quantity 10,000 Quality 256x256px Diversity Ethnicity, gender Metadata The JSON files contain the metadata for each image in a machine-readable format, including: (1) FaceLandmarks: mouth, right_eyebrow, left_eyebrow, right_eye, left_eye, nose, jaw. (2) FaceAttributes: headPose, gender, makeup, emotion, facialHair, hair (hairColor, hairLength, bald), occlusion, ethnicity, eye_color, smile, age

We introduce an annotated dataset of five thousand human labeled pareidolic face images, called ``Faces in Things''. Faces in Things is derived from the LAION-5B dataset and annotated for key face attributes and bounding boxes

The volumetric representation of human interactions is one of the fundamental domains in the development of immersive media productions and telecommunication applications. Particularly in the context of the rapid advancement of Extended Reality (XR) applications, this volumetric data has proven to be an essential technology for future XR elaboration. In this work, we present a new multimodal database to help advance the development of immersive technologies. Our proposed database provides ethically compliant and diverse volumetric data, in particular 27 participants displaying posed facial expressions and subtle body movements while speaking, plus 11 participants wearing head-mounted displays (HMDs). The recording system consists of a volumetric capture (VoCap) studio, including 31 synchronized modules with 62 RGB cameras and 31 depth cameras. In addition to textured meshes, point clouds, and multi-view RGB-D data, we use one Lytro Illum camera for providing light field (LF) data simultaneously. Finally, we also provide an evaluation of our dataset employment with regard to the tasks of facial expression classification, HMDs removal, and point cloud reconstruction. The dataset can be helpful in the evaluation and performance testing of various XR algorithms, including but not limited to facial expression recognition and reconstruction, facial reenactment, and volumetric video. HEADSET and its all associated raw data and license agreement will be publicly available for research purposes.

This dataset consists of 600+ items of faces with different emotions and mixed races that are ready to use for optimizing the accuracy of computer vision models. Human age range from 20 to 60 years old, balance in gender, no occlusion, with head direction (<45 degree up-down and left-right). All of the contents is sourced from PIXTA's stock library of 100M+ Asian-featured images and videos. PIXTA is the largest platform of visual materials in the Asia Pacific region offering fully-managed services, high quality contents and data, and powerful tools for businesses & organizations to enable their creative and machine learning projects. For more details, please refer to the link: https://www.pixta.ai/ Or send your inquiries to contact@pixta.ai

ICT-3DHP is collected using the Microsoft Kinect sensor and contains RGB images and depth maps of about 14k frames, divided in 10 sequences. The image resolution is 640 × 480 pixels. An hardware sensor (Polhemus Fastrack) is exploited to generate the ground truth annotation. The device is placed on a white cap worn by each subject, visible in both RGB and depth frames. Source: POSEidon: Face-from-Depth for Driver Pose Estimation

Description K-pop Idol Dataset - Female (KID-F) is the first dataset of K-pop idol high quality face images. It consists of about 6,000 high quality face images at 512x512 resolution and identity labels for each image. We collected about 90,000 K-pop female idol images and crop the face from each image. And we classified high quality face images. As a result, there are about 6,000 high quality face images in this dataset. There are 300 test datasets for a benchmark. There are no duplicate images between test and train images. Some identities in test images are not duplicated with train images. (It means some test images is new identity to the trained model) Each test images have its degraded pair. You can use these degraded test images for testing face super resolution performance. We also provide identity labels for each image. You can download the csv file from our github # Download You can download dataset from here. Google Drive # Agreement - The use of this software is RESTRICTED to non-commercial research and educational purposes. - All images of the KID-F dataset are obtained from the internet which are not property of EDA(PCEO-AI-CLUB). EDA is not responsible for the content nor the meaning of these images. - You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purposes, any portion of the images and any portion of derived data. - You agree not to further copy, publish or distribute any portion of the KID-F dataset. Except, for internal use at a single site within the same organization it is allowed to make copies of the dataset. - EDA reserves the right to terminate your access to the CelebA dataset at any time.

The MS-EVS Dataset is the first large-scale event-based dataset for face detection. - Regular images (APS) and simulated events (EVS) - Multispectral data (RGB and/or near IR) - Very diverse faces and data quality (for robust face detection) It is divided in four subsets: - N-YoutubeFaces, 3k+ celebrity faces from Youtube videos, in color (simulated events). - N-MobiFace, 80 unedited mobile live color videos (simulated events). - N-SpectralFace, 69 multispectral videos captured in-house (simulated events). - Real-SpectralFace, a few sequences with real multispectral events (Grayscale and Infrared). | Dataset | APS | Simulated EVS | Real EVS | Data | |:---------------------:|:-------:|:-----------------:|:------------:|:--------------------------------------:| | N-YoutubeFaces | x | x | | Color (RGB) | | N-MobiFace | x | x | | Color (RGB) | | N-SpectralFace | x | x | | Multispectral (9 visible + 1 infrared) | | Real-SpectralFace | x | | x | Multispectral (1 visible + 1 infrared) |

Facial landmark detection is a cornerstone in many facial analysis tasks such as face recognition, drowsiness detection, and facial expression recognition. Numerous methodologies were introduced to achieve accurate and efficient facial landmark localization in visual images. However, there are only several works that address facial landmark detection in thermal images. The main challenge is the limited number of annotated datasets. In this work, we present a thermal face dataset with annotated face bounding boxes and facial landmarks. The dataset contains 2,556 thermal images of 142 individuals, where each thermal image is paired with the corresponding visual image. To the best of our knowledge, our dataset is the largest in terms of the number of individuals. In addition, our dataset can be employed for tasks such as thermal-to-visual image translation, thermal-visual face recognition, and others. We trained two models for the facial landmark detection task to show the efficacy of our dataset. The first model is a classic machine learning model based on an ensemble of regression trees. The second model is a deep learning model based on the U-net architecture. The dataset, annotations, source code, and pre-trained models are publicly available to advance research in thermal face analysis.

Face detection and subsequent localization of facial landmarks are the primary steps in many face applications. Numerous algorithms and benchmark datasets have been introduced to develop robust models for the visible domain. However, varying conditions of illumination still pose challenging problems. In this regard, thermal cameras are employed to address this problem, because they operate on longer wavelengths. However, thermal face and facial landmark detection in the wild is an open research problem because most of the existing thermal datasets were collected in controlled environments. In addition, many of them were not annotated with face bounding boxes and facial landmarks. In this work, we present a thermal face dataset with manually labeled bounding boxes and facial landmarks to address these problems. The dataset contains 9,982 images of 147 subjects collected under controlled and uncontrolled conditions. As a baseline, we trained the YOLOv5 object detection model and its adaptation for face detection, YOLO5Face, on our dataset. In addition to our test set, we evaluated the models on the external RWTH-Aachen thermal face dataset to show the efficacy of our dataset. We have made the dataset, source code, and pre-trained models publicly available at https://github.com/IS2AI/TFW to bolster research in thermal face analysis.

Toronto NeuroFace Dataset: A New Dataset for Facial Motion Analysis in Individuals with Neurological Disorders Toronto NeuroFace Dataset is a public dataset with videos of oro-facial gestures performed by individuals with oro-facial impairment due to neurological disorders, such as amyotrophic lateral sclerosis (ALS) and stroke. Perceptual clinical scores from trained clinicians are provided as metadata. Manual annotation of facial landmarks is also provided for a subset of over 3300 frames.