Proceedings ArticleDOI
A convolutional neural network cascade for face detection
Haoxiang Li,Zhe Lin,Xiaohui Shen,Jonathan Brandt,Gang Hua +4 more
- pp 5325-5334
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TLDR
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.Abstract:
In real-world face detection, large visual variations, such as those due to pose, expression, and lighting, demand an advanced discriminative model to accurately differentiate faces from the backgrounds. Consequently, effective models for the problem tend to be computationally prohibitive. To address these two conflicting challenges, we propose a cascade architecture built on convolutional neural networks (CNNs) with very powerful discriminative capability, while maintaining high performance. The proposed CNN cascade operates at multiple resolutions, quickly rejects the background regions in the fast low resolution stages, and carefully evaluates a small number of challenging candidates in the last high resolution stage. To improve localization effectiveness, and reduce the number of candidates at later stages, we introduce a CNN-based calibration stage after each of the detection stages in the cascade. The output of each calibration stage is used to adjust the detection window position for input to the subsequent stage. The proposed method runs at 14 FPS on a single CPU core for VGA-resolution images and 100 FPS using a GPU, and achieves state-of-the-art detection performance on two public face detection benchmarks.read more
Citations
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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.
Proceedings ArticleDOI
Cascade R-CNN: Delving Into High Quality Object Detection
Zhaowei Cai,Nuno Vasconcelos +1 more
TL;DR: Cascade R-CNN as mentioned in this paper proposes a multi-stage object detection architecture, which consists of a sequence of detectors trained with increasing IoU thresholds, to be sequentially more selective against close false positives.
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.
Journal ArticleDOI
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
TL;DR: A deep cascaded multitask framework that exploits the inherent correlation between detection and alignment to boost up their performance and achieves superior accuracy over the state-of-the-art techniques on the challenging face detection dataset and benchmark.
Journal ArticleDOI
Deep Learning for Generic Object Detection: A Survey
Li Liu,Li Liu,Wanli Ouyang,Xiaogang Wang,Paul Fieguth,Jie Chen,Xinwang Liu,Matti Pietikäinen +7 more
TL;DR: A comprehensive survey of the recent achievements in this field brought about by deep learning techniques, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics.
References
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Proceedings ArticleDOI
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Proceedings ArticleDOI
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