UniDataPro/phone-and-webcam-video

Phone and Webcam videos

Dataset comprises 30,952 videos featuring over 3869 individuals who each recorded 4 videos while pronouncing a set of numbers. This dataset is designed to facilitate research in biometric verification, face recognition, and action recognition. The videos include 2 recordings on mobile devices, each lasting approximately 30 seconds and 8 seconds, and 2 webcam recordings with the same durations.

By utilizing this dataset, developers and researchers can enhance their understanding of human activity and improving object detection. - Get the data

Examples of data

This extensive collection provides high-quality video recordings that are ideal for training and testing various vision models and learning algorithms. The dataset is a valuable resource for developing and evaluating detection algorithms and object tracking systems.

Frequently Asked Questions

What is this phone and webcam dataset used for?

This phone and webcam dataset is designed for training AI models in face recognition, liveness detection, biometric authentication, and computer vision. The combination of smartphone and webcam recordings makes it suitable for developing identity verification systems, presentation attack detection (PAD), and facial analysis models that operate across different capture devices.

How was the data collected?

The recordings were collected through a standardized acquisition process using both webcams and mobile phones. Capturing the same participant with different devices provides consistent cross-device facial data for evaluating the robustness of biometric and computer vision models.

What metadata is included with the dataset?

Each recording includes metadata such as participant ID, gender, ethnicity, smartphone type, and webcam model. These annotations enable researchers to analyze model performance across different demographics and hardware configurations while supporting fairness and robustness evaluations.

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This dataset is an invaluable asset for researchers aiming to achieve higher detection accuracy and improve the performance of face recognition systems, ultimately contributing to advancements in biometric security and liveness detection technologies.

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