README.md

---
license: cc-by-nc-nd-4.0
task_categories:
- image-to-video
- image-to-image
- video-classification
- image-classification
- image-feature-extraction
language:
- en
tags:
- biology
- finance
- code
- legal
---
# Selfies and video dataset
4000 people in this dataset. Each person took a selfie on a webcam, took a selfie on a mobile phone. In addition, people recorded video from the phone and from the webcam, on which they pronounced a given set of numbers.
Includes folders corresponding to people in the dataset. Each folder includes 8 files (4 images and 4 videos).

# Get the dataset

### This is just an example of the data

Leave a request on [**https://trainingdata.pro/datasets**](https://trainingdata.pro/datasets/selfie-and-video?utm_source=huggingface&utm_medium=cpc&utm_campaign=selfie_and_video) to discuss your requirements, learn about the price and buy the dataset.

# File with the extension .csv
includes the following information for each media file:

- **SetId**: a unique identifier of a set of 8 media files,
- **WorkerId**: the identifier of the person who provided the media file,
- **Country**: the country of origin of the person,
- **Age**: the age of the person,
- **Gender**: the gender of the person,
- **Type**: the type of media file
- **Link**: the URL to access the media file


# Folder "img" with media files
- containg all the photos and videos
- which correspond to the data in the .csv file

**How it works**: *go to the first folder and you will make sure that it contains media files taken by a person whose parameters are specified in the first 8 lines of the .csv file.*

## [**TrainingData**](https://trainingdata.pro/datasets/selfie-and-video?utm_source=huggingface&utm_medium=cpc&utm_campaign=selfie_and_video) provides high-quality data annotation tailored to your needs

More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**

TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**

*keywords: biometric system, 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 videos dataset, human faces dataset, machine learning, video-to-image, re-identification, verification models, video dataset, video classification, video recognition, photos and videos*