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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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
Synergistic face detection and pose estimation with energy-based models
TL;DR: A novel method for real-time, simultaneous multi-view face detection and facial pose estimation that employs a convolutional network to map face images to points on a manifold, parametrized by pose, and non-face images to Points far from that manifold is described.
Proceedings ArticleDOI
Names and faces in the news
Tamara L. Berg,Alexander C. Berg,Jaety Edwards,Michael Maire,Ryan White,Yee Whye Teh,Erik Learned-Miller,David Forsyth +7 more
TL;DR: It is shown quite good face clustering is possible for a dataset of inaccurately and ambiguously labelled face images, obtained by applying a face finder to approximately half a million captioned news images.
Journal ArticleDOI
Synergistic Face Detection and Pose Estimation with Energy-Based Models
TL;DR: In this article, a convolutional network is used to map images of faces to points on a low-dimensional manifold parametrized by pose, and images of non-faces to points far away from that manifold.
Proceedings ArticleDOI
Scalable near identical image and shot detection
TL;DR: Two novel schemes for near duplicate image and video-shot detection based on global hierarchical colour histograms, using Locality Sensitive Hashing for fast retrieval and local feature descriptors, are proposed and compared.
Proceedings ArticleDOI
Detecting image near-duplicate by stochastic attributed relational graph matching with learning
Dong-Qing Zhang,Shih-Fu Chang +1 more
TL;DR: A part-based image similarity measure derived from stochastic matching of Attributed Relational Graphs that represent the compositional parts and part relations of image scenes that outperforms the prior approaches with large margin.