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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Journal ArticleDOI
Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks
TL;DR: Zhang et al. as mentioned in this paper proposed a deep cascaded multitask framework that exploits the inherent correlation between detection and alignment to boost up their performance, which leverages a cascaded architecture with three stages of carefully designed deep convolutional networks to predict face and landmark location in a coarse-to-fine manner.
Journal ArticleDOI
Object Detection With Deep Learning: A Review
TL;DR: In this article, a review of deep learning-based object detection frameworks is provided, focusing on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further.
Proceedings ArticleDOI
Face detection, pose estimation, and landmark localization in the wild
Xiangxin Zhu,Deva Ramanan +1 more
TL;DR: It is shown that tree-structured models are surprisingly effective at capturing global elastic deformation, while being easy to optimize unlike dense graph structures, in real-world, cluttered images.
Proceedings ArticleDOI
WIDER FACE: A Face Detection Benchmark
TL;DR: There is a gap between current face detection performance and the real world requirements, and the WIDER FACE dataset, which is 10 times larger than existing datasets is introduced, which contains rich annotations, including occlusions, poses, event categories, and face bounding boxes.
Proceedings ArticleDOI
A convolutional neural network cascade for face detection
TL;DR: This work proposes a cascade architecture built on convolutional neural networks (CNNs) with very powerful discriminative capability, while maintaining high performance, and introduces a CNN-based calibration stage after each of the detection stages in the cascade.
References
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Journal Article
A New Color Image Database for Benchmarking of Automatic Face Detection and Human Skin Segmentation Techniques
TL;DR: A new color face image database for benchmarking of automatic face detection algorithms and human skin segmentation techniques, named the VT-AAST image database, and is divided into four parts.
Proceedings ArticleDOI
Rotation invariant face detection using a model-based clustering algorithm
TL;DR: A model-based clustering algorithm for locating frontal views of human faces with in-plane rotation in complex scenes, which can describe the arbitrary shape of the distributions efficiently in a feature space is presented.
Book ChapterDOI
OBJCUT for face detection
TL;DR: This paper proposes a novel, simple and efficient method for face segmentation which works by coupling face detection and segmentation in a single framework using the OBJCUT formulation.
Proceedings ArticleDOI
Pose invariant face detection
TL;DR: A novel method for pose invariant face detection in color images based on the integration of evidence from various independent sources such as color, frequency response and geometric shape information is presented.