Dataset

This dataset was created by Felipe Menino

Released under Attribution 4.0 International (CC BY 4.0)

Contents

The AR face database contains 100 classes of faces with 26 face images per class with various natural variation and occlusions.

Comparisons of CPU time on AR face database testing with scarves.

Matting (hair&faces) - faces dataset

Accurately estimated foreground object in images. Dataset for editing applications for creating visual effects.

💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on roman@kucev.com to buy the dataset

Content

Includes 2 folders: - images - original images of faces - masks - matting masks for images

💴 Buy the Dataset: This is just an example of the data. Leave a request on roman@kucev.com to discuss your requirements, learn about the price and buy the dataset.

keywords: head segmentation dataset, face-generation, segmentation, human faces, portrait segmentation, human face extraction, image segmentation, annotation, biometric dataset, biometric data dataset, face recognition database, facial recognition, face forgery detection, face shape, ar, augmented reality, face detection dataset, facial analysis, human images dataset, hair segmentation, matting, image matting, computer vision, deep learning, potrait matting, natural image matting

The average recognition rates (%) and the corresponding standard deviations (%) of different algorithms on the test set of the AR face database with sunglasses and scarf occlusions (sub-image size 32×32).

Makeup Detection - face recognition dataset

The dataset consists of photos featuring the same individuals captured in two distinct scenarios - with and without makeup. The dataset contains a diverse range of individuals with various ages, ethnicities and genders. The images themselves would be of high quality, ensuring clarity and detail for each subject.

💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on roman@kucev.com to buy the dataset

In photos with makeup, it is applied to only specific parts of the face, such as eyes, lips, or skin. In photos without makeup, individuals have a bare face with no visible cosmetics or beauty enhancements. These images would provide a clear contrast to the makeup images, allowing for significant visual analysis.

The dataset's possible applications:

• facial recognition
• beauty consultations and personalized recommendations
• augmented reality and filters in photography apps
• social media and influencer marketing
• dermatology and skincare

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F618942%2F19315db53167636ddd6d4e7c3a2b27c0%2FMacBook%20Air%20-%201.png?generation=1689876066594275&alt=media" alt="">

Content

• no_makeup: includes images of people without makeup
• with_makeup: includes images of people wearing makeup. People are the same as in the previous folder, photos are identified by the same name
• .csv file: contains information about people in the dataset

File with the extension .csv

includes the following information for each set of media files:

• no_makeup: link to the photo of a person without makeup,
• with_makeup: link to the photo of the person with makeup,
• part: body part of makeup's application,
• gender: gender of the person,
• age: age of the person,
• country: country of the person

Images for makeup detection might be collected in accordance with your requirements.

💴 Buy the Dataset: This is just an example of the data. Leave a request on roman@kucev.com to discuss your requirements, learn about the price and buy the dataset.

keywords: makeup dataset, makeup detection, before-makeup shot, aftermakeup shot, general face recognition dataset, facial cosmetics database, face recognition system, post-makeup images, lipstick, cosmetic products, automatic facial makeup detection, eye makeup, beauty, cosmetics, biometric dataset, biometric data dataset, face recognition database, face recognition dataset, face detection dataset, facial analysis, human images dataset, image segmentation, images dataset, computer vision, deep learning dataset, augmented reality, ar, human identification, re-identification

The p-values of the pairwise one-tailed Wilcoxon rank sum tests on the test set of the AR face database with sunglasses and scarf occlusions (sub-image size 21×32).

This dataset contains the post-survey measures of the PANAS scale and videoconference fatigue from participants in a 2 (AR face filters: present vs. absent) X 2 (self-view: present vs. absent) dyadic between-subjects experiment, together with the analysis scripts.

Context

CUHK Face Sketch Database (CUFS) is for research on face sketch synthesis and face sketch recognition.

Content

It includes 188 faces from the Chinese University of Hong Kong (CUHK) student database, 123 faces from the AR database, and 295 faces from the XM2VTS database. There are 606 faces in total. For each face, there is a sketch drawn by an artist based on a photo taken in a frontal pose, under normal lighting condition and with a neutral expression. Follow below some pairs (photo and sketch) provided by the authors respectively from the ARFACE, CUHK Student database and XM2VTS:

Acknowledgements

If you use this package, please cite the authors of the database:

@inproceedings{zhang2011coupled, title={Coupled information-theoretic encoding for face photo-sketch recognition}, author={Zhang, Wei and Wang, Xiaogang and Tang, Xiaoou}, booktitle={Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on}, pages={513--520}, year={2011}, organization={IEEE} }

This database is named Sample Face Q.Dsent Database (SFQ2D) and is used to test the face recognition model in making a face-based student attendance system taken from 5 Informatics students of Muhammadiyah University Semarang with 5 shooting positions, namely front, right, left, top, and bottom.

Description

This dataset contains raw scores in plain text format of several biometric (face and speaker) recognition systems applied on several datasets such as BANCA, Arface, FRGC, GBU, LFW, Multi-PIE, MOBIO, CAS-PEAL, NIST SRE 2012.

The biometric recognition systems are described in the aforementioned manuscript and encompasses Gaussian mixture models, inter-session variability modelling, joint factor analysis and probabilistic linear discriminant analysis.

The databases considered are the following ones:

• BANCA
• AR face database
• Face Recognition Grand Challenge version 2
• The Good, The Bad and the Ugly
• Labeled Faces in the Wild
• Multi-PIE
• MOBIO
• CAS-PEAL
• NIST Speaker Recognition Evaluation 2012

These scores allow to replicate easily and quickly the plots of the manuscript by using the following package:
http://pypi.python.org/pypi/xbob.thesis.elshafey2014

Citation

If you use this dataset in your publication, we would appreciate that you cite the following thesis:

Laurent El Shafey, “Scalable Probabilistic Models for Face and Speaker Recognition”, PhD thesis, 2014.
http://publications.idiap.ch/index.php/publications/show/2830

This dataset consists of 19,163 images divided into 6 categories: - glasses - hand - mask - none - other - sunglasses

The images are all named using only numbers, each being unique . There ar various sizes and dimensions. The dataset is not balanced with majority of the images being in the class "None" and "Mask"

PGU-Face dataset credit: salari, seyed reza; Rostami, Habib (2016), “Pgu-Face: a dataset of partially covered facial images”, Mendeley Data, V1, doi: 10.17632/znpyrgbfdr.1 https://data.mendeley.com/datasets/znpyrgbfdr/1

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Face refers to the areal (polygon) topological primitives that make up MTDB. A face is bounded by one or more edges; its boundary includes only the edges that separate it from other faces, not any interior edges contained within the area of the face. The Topological Faces Shapefile contains the attributes of each topological primitive face. Each face has a unique topological face identifier (TFID) value. Each face in the shapefile includes the key geographic area codes for all geographic areas for which the Census Bureau tabulates data for both the 2020 Census and the annual estimates and surveys. The geometries of each of these geographic areas can then be built by dissolving the face geometries on the appropriate key geographic area codes in the Topological Faces Shapefile.

Tables S1-S13 and Text S1. Table S1. The average recognition rates (%) and the corresponding standard deviations (%) of LCJDSRC under different parameters on the validation set of the ORL face database (sub-image size is 32×32). Table S2. The average recognition rates (%) and the corresponding standard deviations (%) of LCJDSRC under different parameters on the validation set of the ORL face database (sub-image size is 21×32). Table S3. The average recognition rates (%) and the corresponding standard deviations (%) of LCJDSRC under different parameters on the validation set of the ORL face database (sub-image size is 16×32). Table S4. The average recognition rates (%) and the corresponding standard deviations (%) of LCJDSRC under different parameters on the validation set of the ORL face database (sub-image size is 16×21). Table S5. The average recognition rates (%) and the corresponding standard deviations (%) of LCJDSRC under different parameters on the validation set of the Extended YaleB face database (sub-image size is 32×32). Table S6. The average recognition rates (%) and the corresponding standard deviations (%) of LCJDSRC under different parameters on the validation set of the Extended YaleB face database (sub-image size is 21×32). Table S7. The average recognition rates (%) and the corresponding standard deviations (%) of LCJDSRC under different parameters on the validation set of the AR face database (sub-image size is 32×32). Table S8. The average recognition rates (%) and the corresponding standard deviations (%) of LCJDSRC under different parameters on the validation set of the AR face database (sub-image size is 21×32). Table S9. The average recognition rates (%) and the corresponding standard deviations (%) of LCJDSRC under different parameters on the validation set of the AR face database with sunglasses occlusion (sub-image size is 32×32). Table S10. The average recognition rates (%) and the corresponding standard deviations (%) of LCJDSRC under different parameters on the validation set of the AR face database with scarf occlusion (sub-image size is 32×32). Table S11. The average recognition rates (%) and the corresponding standard deviations (%) of LCJDSRC under different parameters on the validation set of the AR face database with sunglasses occlusion (sub-image size is 21×32). Table S12. The average recognition rates (%) and the corresponding standard deviations (%) of LCJDSRC under different parameters on the validation set of the AR face database with scarf occlusion (sub-image size is 21×32). Table S13. The average recognition rates (%) and the corresponding standard deviations (%) of LCJDSRC under different parameters on the validation set of the LFW face database (sub-image size is 32×32). Text S1. The derivation process of Equation (16). (DOC)

amitkp621/AR-3 dataset hosted on Hugging Face and contributed by the HF Datasets community

amitkp621/AR-7 dataset hosted on Hugging Face and contributed by the HF Datasets community

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Face refers to the areal (polygon) topological primitives that make up MTDB. A face is bounded by one or more edges; its boundary includes only the edges that separate it from other faces, not any interior edges contained within the area of the face. The Topological Faces Shapefile contains the attributes of each topological primitive face. Each face has a unique topological face identifier (TFID) value. Each face in the shapefile includes the key geographic area codes for all geographic areas for which the Census Bureau tabulates data for both the 2020 Census and the annual estimates and surveys. The geometries of each of these geographic areas can then be built by dissolving the face geometries on the appropriate key geographic area codes in the Topological Faces Shapefile.

amitkp621/AR-9 dataset hosted on Hugging Face and contributed by the HF Datasets community

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Face refers to the areal (polygon) topological primitives that make up MTDB. A face is bounded by one or more edges; its boundary includes only the edges that separate it from other faces, not any interior edges contained within the area of the face. The Topological Faces Shapefile contains the attributes of each topological primitive face. Each face has a unique topological face identifier (TFID) value. Each face in the shapefile includes the key geographic area codes for all geographic areas for which the Census Bureau tabulates data for both the 2020 Census and the annual estimates and surveys. The geometries of each of these geographic areas can then be built by dissolving the face geometries on the appropriate key geographic area codes in the Topological Faces Shapefile.

The p-values of the pairwise one-tailed Wilcoxon rank sum tests on the test set of the AR face database.