Face Detection Method Based on Cascaded Convolutional Networks

Qi, Rong; Jia, Rui-Sheng; Mao, Qi-Chao; Sun, Hong-Mei; Zuo, Ling-Qun

doi:10.1109/access.2019.2934563

Cited by 28 publications

(19 citation statements)

References 26 publications

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“…True Positive Rate False Positive=2000 Multiscale Cascade [26] 85.67% Hyperface [27] 90.88% DP2MFD [28] 91.73% LDCF+ [29] 93.38% MTCNN [9] 95.04% Qi et.al. [30] 95.10% LRNet [31] 95.10% Proposed Method 96.35%…”

Section: Methodsmentioning

confidence: 99%

Face Detection Based on Receptive Field Enhanced Multi-Task Cascaded Convolutional Neural Networks

Yang

Wu³

2020

IEEE Access

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With the continuous development of deep learning, face detection methods have made the greatest progress. For real-time detection, cascade CNN based on the lightweight model is still the dominant structure that predicts face in a coarse-to-fine manner with strong generalization ability. Compared to other methods, it is not required for a fixed size of the input. However, MTCNN still has poor performance in detecting tiny targets. To improve model generalization ability, we propose a Receptive Field Enhanced Multi-Task Cascaded CNN. This network takes advantage of the Inception-V2 block and receptive field block to enhance the feature discriminability and robustness for small targets. The experimental results show that the performance of our network is improved by 1.08% on the AFW, 2.84% on the PASCAL FACE, 1.31% on the FDDB, and 2.3%, 2.1%, and 6.6% on the three sub-datasets of the WIDER FACE benchmark in comparison with MTCNN respectively. Furthermore, our structure uses 16% fewer parameters. INDEX TERMS Face detection, cascade convolutional neural networks, receptive field.

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Section: Methodsmentioning

confidence: 99%

Face Detection Based on Receptive Field Enhanced Multi-Task Cascaded Convolutional Neural Networks

Yang

Wu³

2020

IEEE Access

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“…Besides, a novel supervised transformer network was designed to relieve the pose variations problem [14]. [15] design a cascaded framework, which consist of three stage,to progressively improve the face detection performance. The authors of [16] proposed an multiple-branches framework to focus on the accurate detection of small faces.…”

Section: A Face Detectionmentioning

confidence: 99%

A Novel Detection Framework About Conditions of Wearing Face Mask for Helping Control the Spread of COVID-19

et al. 2021

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Properly wearing a face mask has become an effective way to limit the COVID-19 transmission. In this work, we target at detecting the fine-grained wearing state of face mask: face without mask, face with wrong mask, face with correct mask. This task has two main challenging points: 1) absence of practical datasets, and 2) small intra-class distance and large inter-class distance. For the first challenging point, we introduce a new practical dataset covering various conditions, which contains 8635 faces with different wearing status. For the second challenging point, we propose a novel detection framework about conditions of wearing face mask, named Context-Attention R-CNN, which enlarge the intra-class distance and shorten inter-class distance by extracting distinguishing features. Specifically, we first extract the multiple context feature for region proposals, and use attention module to weight these context feature from channel and spatial levels. And then, we decoupling the classification and localization branches to extract more appropriate feature for these two tasks respectively. Experiments show that the Context-Attention R-CNN achieves 84.1% mAP on our proposed dataset, outperforming Faster R-CNN by 6.8 points. Moreover, Context-Attention R-CNN still exceed some state-of-the-art single-stage detectors.

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“…The recently proposed face detection methods use single stage network which lacks the generalization ability as well as require fixed size images. In [15] the authors proposed a cascaded deep convolutional network to overcome these challenges. The proposed method performs two tasks in detection stage-face classification and bounding box regression.…”

Section: Deep Learning In Face Detectionmentioning

confidence: 99%

Performance Analysis of Different Loss Function in Face Detection Architectures

Ferdous

Arifeen

Eiko

et al. 2020

Advances in Intelligent Systems and Computing

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Masked face detection is a challenging task due to the occlusions created by the masks. Recent studies show that deep learning models can achieve effective performance for not only occluded faces but also for unconstrained environments, illuminations or various poses. In this study, we have addressed the problem of occlusion due to wearing masks in masked face detection technique in deep transfer learning method. We have also reviewed the recent deep learning models for face detection and considered VGG16, VGG19, MobileNet and DenseNet as our underlying masked face detection models. Moreover, we have prepared a dataset containing masked face and without mask from 120 individuals and enhanced the dataset using augmentation. After training the deep learning models with our own dataset, we have analysed the performance of the deep learning models for several types of loss functions. From the experiment, it is clear that all the deep learning models perform well in terms of classification losses like categorical cross entropy loss and KL divergence loss.

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Face Detection Method Based on Cascaded Convolutional Networks

Cited by 28 publications

References 26 publications

Face Detection Based on Receptive Field Enhanced Multi-Task Cascaded Convolutional Neural Networks

Face Detection Based on Receptive Field Enhanced Multi-Task Cascaded Convolutional Neural Networks

A Novel Detection Framework About Conditions of Wearing Face Mask for Helping Control the Spread of COVID-19

Performance Analysis of Different Loss Function in Face Detection Architectures

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