Age Difference Images Dataset for Facial Recognition

Dataset comprises 10,557 images of 2,627 unique individuals captured at different stages of their lives, showcasing the same people across various ages. It is designed for research in age estimation, facial aging analysis, and age progression modeling, providing a robust foundation for machine learning models and statistical analysis.

By leveraging this dataset, researchers and developers can explore age differences, gender variations, and age distributions across different age groups, enabling advancements in face recognition, biometrics, and human aging studies. - Get the data

Each entry highlights significant differences in facial features over time, allowing researchers to study age progression patterns, aging gaps between genders, and variations across different life stages. The dataset’s high-quality images and structured metadata support precise age estimation models, cross-age face verification, and synthetic aging simulations.

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For researchers seeking a reliable, large-scale dataset on human aging, this collection offers diverse, well-annotated data with real-world applicability.

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Aggressive Behavior Video Classification, Object Detection dataset

WARNING: People in the videos exhibit aggressive behavior

The dataset with videos depicting people exhibiting aggressive and non-aggressive behavior is intended for classification purposes. It consists of a collection of video files that capture various individuals engaging in different activities and displaying distinct behavioral patterns and CSV-file with classification.

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Aggressive Behavior Video Classification Dataset can have multiple applications, such as surveillance systems, security modules, or social behavior analysis platforms.

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Dataset structure

The dataset consists of: - files: folder with videos with people exhibiting aggressive and non-aggressive behaviour (subfolders "aggressive" and "non_aggressive" respectively), - .csv file: path of each video in the "files" folder and classification of the behavoir

People Behavior Video Classification might be made in accordance with your requirements.

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This dataset contains reaction time data for an affective priming task where participants were asked to respond to pleasant, unpleasant, and COVID-19 related words. 40 participants who are self-described native English speakers and self-identify as vaccine-hesitant or pro-vaccination are included.

18,000+ images from 2,000+ individuals: 6 selfies + 2 official ID photos per person. Ages 18-65, balanced gender, all major ethnicities. Ideal for KYC facial verification, identity matching, and document authentication AI training

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Dataset comprising real-life selfies and official ID photos, ideal for facial verification research. Each participant contributes six recent selfies and two official ID pictures from ID documents such as passport, ID card, residence permit, or driver's license

This dataset covers a wide range of ages (18-65) and ethnicities, featuring selfies with diverse backgrounds, clothing, and facial expressions.

Dataset Description:

Each participant provides: - Six selfies taken with mobile devices and web cameras. - Two images from official documents like passports and ID cards. - Selfies showcase a variety of backgrounds and clothing styles

Overall dataset Parameters: - Over 2,000 individuals contributed to this dataset - Over 18,000 images collected - Gender distribution is balanced. - Age range: 18-65 years old. - Represents all major nationalities, including Caucasian, African, East and South Asian, and Latin American ethnicities

Valid Official ID documents for this task: - Passport - ID card - Residence permit - Driver's license

Full version of dataset is availible for commercial usage - leave a request on our website Axonlabs to purchase the dataset 💰

Potential Use Cases:

Facial Verification: This dataset is invaluable for training facial verification systems, enabling them to authenticate whether a selfie matches the individual in the ID photo. These systems are crucial in security-sensitive areas like online banking and mobile payments

Facial Matching: With a wide range of selfies showcasing diverse backgrounds, poses, and expressions, this dataset is instrumental in training algorithms to recognize faces amidst such variations. As a result, it aids in the advancement of more resilient facial recognition systems

keywords: biometric system, biometric system attacks, biometric dataset, face recognition database, face recognition dataset, face detection dataset, facial analysis, object detection dataset, deep learning datasets, computer vision datset, human images dataset, human faces dataset

Familiist (pro-natalist) communities in social net

Exploring Pregnancy, Childhood, Motherhood, and Paternity

By [source]

About this dataset

This dataset presents an unparalleled collection of comments from Russian-speaking Familiist pro-natalist communities on the social media platform VKontakte. With its focus on pregnancy, childbirth, motherhood, and fatherhood topics, this vast archive offers researchers a powerful data set in which to assess the perception of modern family life within these platforms.

The comprehensive dataset contains UTF-8 formatted .csv files with each entry containing unique identifiers for both the post and user as well as several pre-processed versions of each comment such as removing punctuation marks and lowercasing words, text stemming and lemmatization. Additional data includes datetime stamps and likes counts of every comment.

This cutting edge collection provides a window into contemporary debate over parenthood matters that were previously unobtainable by researchers – uncovering a valuable perspective into how mainstream views are expressed within these online communities. By delving deep into this dataset you can help shape our understanding of how modern online discussion forums perceive pregnancy, childhood, motherhood, paternity - furthering all academic research in these interconnected fields!

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How to use the dataset

This dataset is includes comments from Familiist pro-natalist communities in the social network VKontakte and provides valuable insights into the different topics discussed in these communities such as pregnancy, childhood, motherhood, paternity and more. While this dataset is primarily intended for data analysis purposes by research laboratories, it can also be used by individuals wanting to learn more about these topics or social networks.

The data is provided in .csv format and consists of several columns: link_author (unique identifier of the user who posted the comment); gender (gender of the user); link_comment (unique identifier of the comment); date_time (date and time when the comment was posted); text (the text of the comment after pre-processing); likes (number of likes received by a comment); text_prep (the text after pre-processing), including removing punctuation marks; text stemmized, which includes stemming; text sw for stopwords removal;and finally lemmatization.

By using this data set you will have access to important information related to pregnancy, childhood, motherhood and fatherhood within Familiist pro-natalist social networks users’ discussions on VKontakte. Additionally you will be able to discover new trends on those topics as well as quantity metrics such as number of views comments receive. Use this data set creatively!

Research Ideas

• Analyzing trends in gender representation amongst users on Familiist Pro-Natalism communities, to understand the male/female conversation dynamics and differences in topics or engagement.
• Developing sentiment analysis models for automatically detecting the mood of comments posted, to gauge pro-natalism or anti-natalist sentiments.
• Tracking changes in the likes received for different comments over time, to study popular topics or themes within this domain and their associated engagement levels

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: vk_posts_stem_lemm.csv | Column name | Description | |:-----------------|:----------------------------------------------------------------------| | link_author | Unique identifier of the user who posted the comment. (String) | | gender | Gender of user who posted comment. (String) | | link_comment | Unique identifier of comment. (String) | | date_time | Date and time when comment was posted. (String) | | text | Text of comment before any preprocessing was done. (String) | | likes | Number of likes that were receive...