CMS-RCNN: Contextual Multi-Scale Region-Based CNN for Unconstrained Face Detection
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A face detection approach named Contextual Multi-Scale Region-based Convolution Neural Network (CMS-RCNN) to robustly solve the problems mentioned above and allows explicit body contextual reasoning in the network inspired from the intuition of human vision system.Abstract:
Robust face detection in the wild is one of the ultimate components to support various facial related problems, i.e., unconstrained face recognition, facial periocular recognition, facial landmarking and pose estimation, facial expression recognition, 3D facial model construction, etc. Although the face detection problem has been intensely studied for decades with various commercial applications, it still meets problems in some real-world scenarios due to numerous challenges, e.g., heavy facial occlusions, extremely low resolutions, strong illumination, exceptional pose variations, image or video compression artifacts, etc. In this paper, we present a face detection approach named Contextual Multi-Scale Region-based Convolution Neural Network (CMS-RCNN) to robustly solve the problems mentioned above. Similar to the region-based CNNs, our proposed network consists of the region proposal component and the region-of-interest (RoI) detection component. However, far apart of that network, there are two main contributions in our proposed network that play a significant role to achieve the state-of-the-art performance in face detection. First, the multi-scale information is grouped both in region proposal and RoI detection to deal with tiny face regions. Second, our proposed network allows explicit body contextual reasoning in the network inspired from the intuition of human vision system. The proposed approach is benchmarked on two recent challenging face detection databases, i.e., the WIDER FACE Dataset which contains high degree of variability, as well as the Face Detection Dataset and Benchmark (FDDB). The experimental results show that our proposed approach trained on WIDER FACE Dataset outperforms strong baselines on WIDER FACE Dataset by a large margin, and consistently achieves competitive results on FDDB against the recent state-of-the-art face detection methods.read more
Citations
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Proceedings ArticleDOI
Finding Tiny Faces
Peiyun Hu,Deva Ramanan +1 more
TL;DR: In this article, the authors explore three aspects of the problem in the context of finding small faces: the role of scale invariance, image resolution, and contextual reasoning, and train separate detectors for different scales.
Proceedings ArticleDOI
Face Detection with the Faster R-CNN
Huaizu Jiang,Erik Learned-Miller +1 more
TL;DR: By training a Faster R-CNN model on the large scale WIDER face dataset, this paper reports state-of-the-art results on the WIDER test set as well as two other widely used face detection benchmarks, FDDB and the recently released IJB-A.
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Finding Tiny Faces
Peiyun Hu,Deva Ramanan +1 more
TL;DR: The role of scale in pre-trained deep networks is explored, providing ways to extrapolate networks tuned for limited scales to rather extreme ranges and demonstrating state-of-the-art results on massively-benchmarked face datasets.
Journal ArticleDOI
Recent Advances in Deep Learning for Object Detection
TL;DR: A comprehensive survey of recent advances in visual object detection with deep learning can be found in this article, where the authors systematically analyze the existing object detection frameworks and organize the survey into three major parts: detection components, learning strategies, and applications and benchmarks.
Proceedings ArticleDOI
S^3FD: Single Shot Scale-Invariant Face Detector
TL;DR: S3FD as mentioned in this paper proposes a scale-equitable face detection framework to handle different scales of faces well and improves the recall rate of small faces by a scale compensation anchor matching strategy.
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