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
Citations
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Proceedings ArticleDOI
UnitBox: An Advanced Object Detection Network
TL;DR: UnitBox as mentioned in this paper proposes an intersection over union (IoU$) loss function for bounding box prediction, which regresses the four bounds of a predicted box as a whole unit.
Proceedings ArticleDOI
Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A
Brendan Klare,Ben Klein,Emma Taborsky,Austin Blanton,Jordan Cheney,Kristen Allen,Patrick J. Grother,Alan Mah,Mark J. Burge,Anil K. Jain +9 more
TL;DR: Baseline accuracies for both face detection and face recognition from commercial and open source algorithms demonstrate the challenge offered by this new unconstrained benchmark.
Proceedings ArticleDOI
RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild
TL;DR: A novel single-shot, multi-level face localisation method, named RetinaFace, which unifies face box prediction, 2D facial landmark localisation and 3D vertices regression under one common target: point regression on the image plane.
Journal ArticleDOI
300 Faces In-The-Wild Challenge
Christos Sagonas,Epameinondas Antonakos,Georgios Tzimiropoulos,Stefanos Zafeiriou,Maja Pantic +4 more
TL;DR: This paper proposes a semi-automatic annotation technique that was employed to re-annotate most existing facial databases under a unified protocol, and presents the 300 Faces In-The-Wild Challenge (300-W), the first facial landmark localization challenge that was organized twice, in 2013 and 2015.
A Survey of Recent Advances in Face Detection
Cha Zhang,Zhengyou Zhang +1 more
TL;DR: This technical report surveys the recent advances in face detection for the past decade and surveys the various techniques according to how they extract features and what learning algorithms are adopted.
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.