Gender Detection & Classification - face recognition dataset

The dataset is created on the basis of Face Mask Detection dataset

Dataset Description:

The dataset comprises a collection of photos of people, organized into folders labeled "women" and "men." Each folder contains a significant number of images to facilitate training and testing of gender detection algorithms or models.

The dataset contains a variety of images capturing female and male individuals from diverse backgrounds, age groups, and ethnicities.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F1c4708f0b856f7889e3c0eea434fe8e2%2FFrame%2045%20(1).png?generation=1698764294000412&alt=media" alt="">

This labeled dataset can be utilized as training data for machine learning models, computer vision applications, and gender detection algorithms.

💴 For Commercial Usage: Full version of the dataset includes 376 000+ photos of people, leave a request on TrainingData to buy the dataset

Metadata for the full dataset:

• assignment_id - unique identifier of the media file
• worker_id - unique identifier of the person
• age - age of the person
• true_gender - gender of the person
• country - country of the person
• ethnicity - ethnicity of the person
• photo_1_extension, photo_2_extension, photo_3_extension, photo_4_extension - photo extensions in the dataset
• photo_1_resolution, photo_2_resolution, photo_3_extension, photo_4_resolution - photo resolution in the dataset

OTHER BIOMETRIC DATASETS:

• Anti Spoofing Real Dataset
• Antispoofing Replay Dataset
• Selfies, ID Images dataset (5591 sets of 15 files)
• Selfies and video dataset (4 052 sets)
• Dataset of bald people, 5000 images

💴 Buy the Dataset: This is just an example of the data. Leave a request on https://trainingdata.pro/datasets to learn about the price and buy the dataset

Content

The dataset is split into train and test folders, each folder includes: - folders women and men - folders with images of people with the corresponding gender, - .csv file - contains information about the images and people in the dataset

File with the extension .csv

• file: link to access the file,
• gender: gender of a person in the photo (woman/man),
• split: classification on train and test

TrainingData provides high-quality data annotation tailored to your needs

keywords: biometric system, biometric system attacks, biometric dataset, face recognition database, face recognition dataset, face detection dataset, facial analysis, gender detection, supervised learning dataset, gender classification dataset, gender recognition dataset

Gender Classification

## Overview

Gender Classification is a dataset for object detection tasks - it contains Man Woman annotations for 1,769 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

myvision/gender-classification dataset hosted on Hugging Face and contributed by the HF Datasets community

Full Body Gender Classification Dataset

## Overview

Full Body Gender Classification Dataset is a dataset for object detection tasks - it contains Man annotations for 1,367 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Gender Classification Yolov8

## Overview

Gender Classification Yolov8 is a dataset for object detection tasks - it contains Male annotations for 1,233 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Analysis of ‘Gender Classification Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/elakiricoder/gender-classification-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

While I was practicing machine learning, I wanted to create a simple dataset that is closely aligned to the real world scenario and gives better results to whet my appetite on this domain. If you are a beginner who wants to try solving classification problems in machine learning and if you prefer achieving better results, try using this dataset in your projects which will be a great place to start.

Content

This dataset contains 7 features and a label column.

long_hair - This column contains 0's and 1's where 1 is "long hair" and 0 is "not long hair". forehead_width_cm - This column is in CM's. This is the width of the forehead. forehead_height_cm - This is the height of the forehead and it's in Cm's. nose_wide - This column contains 0's and 1's where 1 is "wide nose" and 0 is "not wide nose". nose_long - This column contains 0's and 1's where 1 is "Long nose" and 0 is "not long nose". lips_thin - This column contains 0's and 1's where 1 represents the "thin lips" while 0 is "Not thin lips". distance_nose_to_lip_long - This column contains 0's and 1's where 1 represents the "long distance between nose and lips" while 0 is "short distance between nose and lips".

gender - This is either "Male" or "Female".

Acknowledgements

Nothing to acknowledge as this is just a made up data.

Inspiration

It's painful to see bad results at the beginning. Don't begin with complicated datasets if you are a beginner. I'm sure that this dataset will encourage you to proceed further in the domain. Good luck.

--- Original source retains full ownership of the source dataset ---

Dataset

This dataset was created by My Project Dictionary

Contents

This project focuses on real-time gender classification using facial images of Indonesian individuals. We trained a Convolutional Neural Network (CNN) model using a custom dataset containing labeled images of Indonesian males and females. The model was built with TensorFlow/Keras and integrated with OpenCV to support real-time prediction via webcam or video input.

💡 Key Features:

Trained on a local dataset of Indonesian faces

Binary classification: Male 👨 vs Female 👩

Real-time prediction using OpenCV + webcam

Achieved over 95% validation accuracy after training

Ready for deployment or integration into surveillance dashboards

🧠 Use Cases:

Demographic analytics

People counting with gender statistics

Retail or event audience profiling

📁 Dataset:

Custom labeled images from sources like Roboflow and Pixabay

Split into train, validation, and test folders

Augmented for better generalization

🛠 Built With:

TensorFlow / Keras

OpenCV

Python

Flask (for optional dashboard)

This dataset contains audio recordings of 12 different accents across the UK: Northern Ireland (NI), Scotland, Wales (SW), North East England (NE), North West England (NW), Yorkshire and Humber (YAH), East Midlands (EM), West Midlands (WM), East of England (EE), Greater London (GL), South East England (SE), South West England (SW). We split the data into a Male: Female ratio of 1:1, this is labelled with either '_M' for male or '_F' for female within the dataset. The audio dataset was compiled using opensource YouTube videos and it a collation of different accents, the audio files were trimmed for uniformity. The Audio files are of length 30 seconds, with the first 5 seconds and last 5 seconds of the signal being blank. We also resample the audio signals at 8 kHz, again for uniformity and to remove any noise present in the audio signals whilst retaining the underlying characteristics. The intended application of this dataset was to be used in conjunction with a deep neural network for accent and gender classification tasks.

The dataset also contains an unseen dataset of the Google opensource digit dataset, which contains audio files of the digits 1-9. This is included to test any models developed using the original dataset to confirm model performance to data variations.

Gender shades: Intersectional accuracy disparities in commercial gender classification

scraped data from bluesky and mastodon.

Gender-Classifier-7K

Gender-Classifier-7K is a dataset designed for image classification, focusing on distinguishing between female and male individuals. This dataset includes a diverse collection of high-quality images to improve classification accuracy and enhance the model’s overall performance. By offering a well-balanced dataset, it aims to support the development of reliable and fair gender classification models.

  Label Mappings

Mapping of IDs to Labels: {0:… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/Gender-Classifier-7K.

Machine learning models are trained to find patterns in data. NLP models can inadvertently learn socially undesirable patterns when training on gender biased text. In this work, we propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions: bias from the gender of the person being spoken about, bias from the gender of the person being spoken to, and bias from the gender of the speaker. Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information. In addition, we collect a novel, crowdsourced evaluation benchmark of utterance-level gender rewrites. Distinguishing between gender bias along multiple dimensions is important, as it enables us to train finer-grained gender bias classifiers. We show our classifiers prove valuable for a variety of important applications, such as controlling for gender bias in generative models, detecting gender bias in arbitrary text, and shed light on offensive language in terms of genderedness.

Dataset

This dataset was created by Jay Lunia

Released under Apache 2.0

Contents

Fingerprint dataset containing 6000 images of 200 subjects (102 females and 98 males) with 3 images per subject.

Gender Classified Dataset with Masked Face – a versatile resource for AI enthusiasts. It combines FFHQ's high-quality images with Google-scraped pictures, enabling gender classification and facial recognition research, even in mask-wearing scenarios..

Dataset

This dataset was created by Pratham Taluja

Contents

Context

While I was practicing machine learning, I wanted to create a simple dataset that is closely aligned to the real world scenario and gives better results to whet my appetite on this domain. If you are a beginner who wants to try solving classification problems in machine learning and if you prefer achieving better results, try using this dataset in your projects which will be a great place to start.

Content

This dataset contains 7 features and a label column.

long_hair - This column contains 0's and 1's where 1 is "long hair" and 0 is "not long hair". forehead_width_cm - This column is in CM's. This is the width of the forehead. forehead_height_cm - This is the height of the forehead and it's in Cm's. nose_wide - This column contains 0's and 1's where 1 is "wide nose" and 0 is "not wide nose". nose_long - This column contains 0's and 1's where 1 is "Long nose" and 0 is "not long nose". lips_thin - This column contains 0's and 1's where 1 represents the "thin lips" while 0 is "Not thin lips". distance_nose_to_lip_long - This column contains 0's and 1's where 1 represents the "long distance between nose and lips" while 0 is "short distance between nose and lips".

gender - This is either "Male" or "Female".

Acknowledgements

Nothing to acknowledge as this is just a made up data.

Inspiration

It's painful to see bad results at the beginning. Don't begin with complicated datasets if you are a beginner. I'm sure that this dataset will encourage you to proceed further in the domain. Good luck.

The HHD_gender dataset contains 819 handwritten forms written by volunteers of

different educational backgrounds and ages (as young as 11 years old and as old as late

60s), both native and non-native Hebrew speakers.

There are 50 variations of the forms; each form contains a text paragraph with

62 words on average.

For the experiments, the HHD gender dataset was randomly subdivided into training (80%), validation (10%), and test

(10%) sets.

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This database may be used for non-commercial research purpose only.

If you publish material based on this database, we request you to include a reference to the following papers:

[1] I. Rabaev, B. Kurar Barakat, A. Churkin and J. El-Sana. The HHD Dataset.

The 17th International Conference on Frontiers in Handwriting Recognition, pp. 228-233, 2020,

DOI: 10.1109/ICFHR2020.2020.00050

[2] I. Rabaev, M. Litvak, S. Asulin and O.H. Tabibi. Automatic Gender Classification from Handwritten Images: a Case Study, the 19th International Conference on Computer Analysis of Images and Patterns, 2021.

This dataset is based on the work of Liu et al and their paper "Hydraplus-net: Attentive deep features for pedestrian analysis". In our work, we structure the images for a gender classification task based on the gender attribute annotated. Moreover, we pre-process the images to a 75x75 dimension that can be used by pre-trained deep learning models.