RealFace -- Pedestrian Face Dataset

TL;DR

The paper introduces the Real Face Dataset, a large and diverse collection of over 11,000 images with 55,000 faces, designed to evaluate face detection and recognition algorithms in real-world conditions.

Contribution

It provides a comprehensive benchmark dataset with high variability in ambient conditions, aiding the development of robust face detection and recognition methods.

Findings

Dataset covers diverse ambient conditions like lighting, pose, and occlusion.

Enables evaluation of algorithms in practical, challenging scenarios.

Supports benchmarking for real-world face detection and recognition.

Abstract

The Real Face Dataset is a pedestrian face detection benchmark dataset in the wild, comprising over 11,000 images and over 55,000 detected faces in various ambient conditions. The dataset aims to provide a comprehensive and diverse collection of real-world face images for the evaluation and development of face detection and recognition algorithms. The Real Face Dataset is a valuable resource for researchers and developers working on face detection and recognition algorithms. With over 11,000 images and 55,000 detected faces, the dataset offers a comprehensive and diverse collection of real-world face images. This diversity is crucial for evaluating the performance of algorithms under various ambient conditions, such as lighting, scale, pose, and occlusion. The dataset's focus on real-world scenarios makes it particularly relevant for practical applications, where faces may be captured in challenging environments. In addition to its size, the dataset's inclusion of images with a high degree of variability in scale, pose, and occlusion, as well as its focus on practical application scenarios, sets it apart as a valuable resource for benchmarking and testing face detection and recognition methods. The challenges presented by the dataset align with the difficulties faced in real-world surveillance applications, where the ability to detect faces and extract discriminative features is paramount. The Real Face Dataset provides an opportunity to assess the performance of face detection and recognition methods on a large scale. Its relevance to real-world scenarios makes it an important resource for researchers and developers aiming to create robust and effective algorithms for practical applications.