Face and eye detection on hard datasets

Abstract

Face and eye detection algorithms are deployed in a wide variety of applications. Unfortunately, there has been no quantitative comparison of how these detectors perform under difficult circumstances. We created a dataset of low light and long distance images which possess some of the problems encountered by face and eye detectors solving real world problems. The dataset we created is composed of reimaged images (photohead) and semi-synthetic heads imaged under varying conditions of low light, atmospheric blur, and distances of 3m, 50m, 80m, and 200m. This paper analyzes the detection and localization performance of the participating face and eye algorithms compared with the Viola Jones detector and four leading commercial face detectors. Performance is characterized under the different conditions and parameterized by per-image brightness and contrast. In localization accuracy for eyes, the groups/companies focusing on long-range face detection outperform leading commercial applications.

The challenge of face recognition from digital point-and-shoot cameras

“…Two notable aspects of LFW are the shift to images of opportunity, for LFW images on the web, along with a well coordinated and updated website that curates current performance results. Face detection also grows more difficult in less controlled scenarios, and the recent Face Detection on Hard Datasets Competition [12] brought together many groups in a joint effort.…”

Eye detection using discriminatory Haar features and a new efficient SVM

“…ACCEPTED MANUSCRIPT recognition community [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19]. Example challenges in accurate and efficient eye detection include large variations in image illumination, skin color (white, yellow, and black), facial expression (eyes open, partially open, or closed), as well as scale and orientation.…”

Combining Face and Facial Feature Detectors for Face Detection Performance Improvement

“…However, face detectors are still unreliable in different hard scenarios where the pose and illumination are not controlled [8,12], or when a large-scale problem is tackled [4]. Therefore, face and facial element detection keeps being a common topic in the Computer Vision literature.…”