Face-MagNet: Magnifying Feature Maps to Detect Small Faces

Samangouei, Pouya; Chellappa, Rama; Najibi, Mahyar; Davis, Larry S.

doi:10.1109/wacv.2018.00020

Cited by 14 publications

(7 citation statements)

References 31 publications

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“…Easy Medium Hard CMS-RCNN [69] 89.9 87.4 62.9 HR-VGG16 + Pyramid [17] 86.2 84.4 74.9 HR-ResNet101 + Pyramid [17] Comparison with other methods. We compare the results of the proposed method with recent state-of-the-art methods such as SSH [32], Face-MagNet [44], S3FD [65], HR [17], CMS-RCNN [69], MT-CNN [64], LDCF [33], Faceness [59] and Multiscale Cascaded CNN [60]. For the validation set, the results of the proposed method are obtained using single-scale inference as well as image-pyramid based reference (as shown in Table 4).…”

Section: Methodsmentioning

confidence: 99%

DAFE-FD: Density Aware Feature Enrichment for Face Detection

Sindagi

Patel

2019

2019 IEEE Winter Conference on Applications of Computer Vision (WACV)

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Recent research on face detection, which is focused primarily on improving accuracy of detecting smaller faces, attempt to develop new anchor design strategies to facilitate increased overlap between anchor boxes and ground truth faces of smaller sizes. In this work, we approach the problem of small face detection with the motivation of enriching the feature maps using a density map estimation module. This module, inspired by recent crowd counting/density estimation techniques, performs the task of estimating the per pixel density of people/faces present in the image. Output of this module is employed to accentuate the feature maps from the backbone network using a feature enrichment module before being used for detecting smaller faces. The proposed approach can be used to complement recent anchor-design based novel methods to further improve their results. Experiments conducted on different datasets such as WIDER, FDDB and Pascal-Faces demonstrate the effectiveness of the proposed approach.

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Section: Methodsmentioning

confidence: 99%

DAFE-FD: Density Aware Feature Enrichment for Face Detection

Sindagi

Patel

2019

2019 IEEE Winter Conference on Applications of Computer Vision (WACV)

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“…Further, they also proposed a new online hard negative mining strategy to improve the result. Samangouei et al [193] features together, which also allowed the PyramidBox to predict faces at all scales in a single shot; and iii) they introduced a context sensitive structure to increase the capacity of prediction network to improve the final accuracy of output. In addition, they used the method of data-anchor-sampling to augment the training samples across different scales, which increased the diversity of training data for smaller faces.…”

Section: Face Detectionmentioning

confidence: 99%

Recent advances in deep learning for object detection

2020

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Object detection is a fundamental visual recognition problem in computer vision and has been widely studied in the past decades.Visual object detection aims to find objects of certain target classes with precise localization in a given image and assign each object instance a corresponding class label. Due to the tremendous successes of deep learning based image classification, object detection techniques using deep learning have been actively studied in recent years. In this paper, we give a comprehensive survey of recent advances in visual object detection with deep learning. By reviewing a large body of recent related work in literature, we systematically analyze the existing object detection frameworks and organize the survey into three major parts: (i) detection components, (ii) learning strategies, and (iii) applications & benchmarks. In the survey, we cover a variety of factors affecting the detection performance in detail, such as detector architectures, feature learning, proposal generation, sampling strategies, etc.Finally, we discuss several future directions to facilitate and spur future research for visual object detection with deep learning.

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“…e representative algorithms of the latter are S3FD [22] and SSH [23]. Compared with traditional learning methods, detection methods based on deep learning do not require manual feature extraction.…”

Section: Related Workmentioning

confidence: 99%

Driver Fatigue Detection Based on Facial Key Points and LSTM

Chen

Xin

Liu

et al. 2021

Security and Communication Networks

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In recent years, fatigue driving has been a serious threat to the traffic safety, which makes the research of fatigue detection a hotspot field. Research on fatigue recognition has a great significance to improve the traffic safety. However, the existing fatigue detection methods still have room for improvement in detection accuracy and efficiency. In order to detect whether the driver has fatigue driving, this paper proposes a fatigue state recognition algorithm. The method first uses MTCNN (multitask convolutional neural network) to detect human face, and then DLIB (an open-source software library) is used to locate facial key points to extract the fatigue feature vector of each frame. The fatigue feature vectors of multiple frames are spliced into a temporal feature sequence and sent to the LSTM (long short-term memory) network to obtain a final fatigue feature value. Experiments show that compared with other methods, the fatigue state recognition algorithm proposed in this paper has achieved better results in accuracy. The average accuracy of the proposed method in detecting key points of the face is as high as 93%, and the running time is less than half of the ordinary DLIB method.

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Face-MagNet: Magnifying Feature Maps to Detect Small Faces

Cited by 14 publications

References 31 publications

DAFE-FD: Density Aware Feature Enrichment for Face Detection

DAFE-FD: Density Aware Feature Enrichment for Face Detection

Recent advances in deep learning for object detection

Driver Fatigue Detection Based on Facial Key Points and LSTM

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