Open Access
FDDB: A benchmark for face detection in unconstrained settings
Vidit Jain,Erik Learned-Miller +1 more
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TLDR
A new data set of face images with more faces and more accurate annotations for face regions than in previous data sets is presented and two rigorous and precise methods for evaluating the performance of face detection algorithms are proposed.Abstract:
Despite the maturity of face detection research, it remains difficult to compare different algorithms for face detection. This is partly due to the lack of common evaluation schemes. Also, existing data sets for evaluating face detection algorithms do not capture some aspects of face appearances that are manifested in real-world scenarios. In this work, we address both of these issues. We present a new data set of face images with more faces and more accurate annotations for face regions than in previous data sets. We also propose two rigorous and precise methods for evaluating the performance of face detection algorithms. We report results of several standard algorithms on the new benchmark.read more
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Book ChapterDOI
ProgressFace: Scale-Aware Progressive Learning for Face Detection
TL;DR: A novel scale-aware progressive training mechanism to address large scale variations across faces, Inspired by curriculum learning, which gradually learns large-to-small face instances and proposes an auxiliary anchor-free enhancement module to facilitate the learning of small faces.
Posted Content
Learning Grimaces by Watching TV
Samuel Albanie,Andrea Vedaldi +1 more
TL;DR: This paper considers a gameshow in which contestants play to win significant sums of money and develops, using benchmarks such as FER and SFEW 2.0, state-of-the-art deep neural networks for facial expression recognition, showing that pre-training on face verification data can be highly beneficial for this task.
Journal ArticleDOI
Video redaction: a survey and comparison of enabling technologies
TL;DR: The present paper reviews the redaction problem and compares a few state-of-the-art detection, tracking, and obfuscation methods as they relate to redaction and introduces an evaluation metric that is specific to video redaction performance.
Proceedings ArticleDOI
Face and Body Association for Video-Based Face Recognition
TL;DR: To track and associate subjects that appear across frames in multiple shots, this work solves a data association problem using both face and body appearance and shows up to 5% improvement in the identification rate over the state-of-the-art.
Proceedings ArticleDOI
A Real-Time Multi-Task Single Shot Face Detector
TL;DR: This paper proposes a unifying framework to simultaneously detect face, fiducial points, and head pose in real-time and develops a progressive training strategy to overcome the annotation discrepancy across different datasets.
References
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Journal ArticleDOI
Robust Real-Time Face Detection
Paul A. Viola,Michael Jones +1 more
TL;DR: In this paper, a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates is described. But the detection performance is limited to 15 frames per second.
Journal ArticleDOI
The Hungarian method for the assignment problem
TL;DR: This paper has always been one of my favorite children, combining as it does elements of the duality of linear programming and combinatorial tools from graph theory, and it may be of some interest to tell the story of its origin this article.
Proceedings Article
On Spectral Clustering: Analysis and an algorithm
TL;DR: A simple spectral clustering algorithm that can be implemented using a few lines of Matlab is presented, and tools from matrix perturbation theory are used to analyze the algorithm, and give conditions under which it can be expected to do well.
Journal ArticleDOI
Neural network-based face detection
TL;DR: A neural network-based upright frontal face detection system that arbitrates between multiple networks to improve performance over a single network, and a straightforward procedure for aligning positive face examples for training.
Journal ArticleDOI
Detecting faces in images: a survey
TL;DR: In this article, the authors categorize and evaluate face detection algorithms and discuss relevant issues such as data collection, evaluation metrics and benchmarking, and conclude with several promising directions for future research.