S^3FD: Single Shot Scale-Invariant Face Detector

The application of face detection and recognition technology in security monitoring systems has made a huge contribution to public security. Face detection is an essential first step in many face analysis systems. In complex scenes, the accuracy of face detection would be limited because of the missing and false detection of small faces, due to image quality, face scale, light, and other factors. In this paper, a two-level face detection model called SR-YOLOv5 is proposed to address some problems of dense small faces in actual scenarios. The research first optimized the backbone and loss function of YOLOv5, which is aimed at achieving better performance in terms of mean average precision (mAP) and speed. Then, to improve face detection in blurred scenes or low-resolution situations, we integrated image superresolution technology on the detection head. In addition, some representative deep-learning algorithm based on face detection is discussed by grouping them into a few major categories, and the popular face detection benchmarks are enumerated in detail. Finally, the wider face dataset is used to train and test the SR-YOLOv5 model. Compared with multitask convolutional neural network (MTCNN), Contextual Multi-Scale Region-based CNN (CMS-RCNN), Finding Tiny Faces (HR), Single Shot Scale-invariant Face Detector (S3FD), and TinaFace algorithms, it is verified that the proposed model has higher detection precision, which is 0.7%, 0.6%, and 2.9% higher than the top one. SR-YOLOv5 can effectively use face information to accurately detect hard-to-detect face targets in complex scenes.

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Aircraft Detection from Remote Sensing Images using Single Shot Scale-invariant Face Detector with Viridis Saliency Map

2020 International Conference on Mathematics and Computers in Science and Engineering (MACISE) ◽

10.1109/macise49704.2020.00037 ◽

2020 ◽

Author(s):

Teepakorn Lilek ◽

Nagul Cooharojananone

Keyword(s):

Remote Sensing ◽

Saliency Map ◽

Single Shot ◽

Remote Sensing Images ◽

Scale Invariant ◽

Aircraft Detection ◽

Face Detector

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CSSD: Cascade Single Shot Face Detector

2019 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2019.8851713 ◽

2019 ◽

Author(s):

Shuainan Wang ◽

Tong Xu ◽

Wei Li ◽

Haifeng Sun

Keyword(s):

Single Shot ◽

Face Detector

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PyramidBox: A Context-Assisted Single Shot Face Detector

Computer Vision – ECCV 2018 - Lecture Notes in Computer Science ◽

10.1007/978-3-030-01240-3_49 ◽

2018 ◽

pp. 812-828 ◽

Cited By ~ 48

Author(s):

Xu Tang ◽

Daniel K. Du ◽

Zeqiang He ◽

Jingtuo Liu

Keyword(s):

Single Shot ◽

Face Detector

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Small Object Detection Algorithm Based on Feature Pyramid-Enhanced Fusion SSD

Complexity ◽

10.1155/2019/7297960 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Haotian Li ◽

Kezheng Lin ◽

Jingxuan Bai ◽

Ao Li ◽

Jiali Yu

Keyword(s):

Object Detection ◽

Detection Rate ◽

Detection Algorithm ◽

Single Shot ◽

Small Object ◽

Feature Maps ◽

Scale Invariant ◽

Feature Pyramid ◽

Small Object Detection ◽

Detection And Localization

In order to improve the detection rate of the traditional single-shot multibox detection algorithm in small object detection, a feature-enhanced fusion SSD object detection algorithm based on the pyramid network is proposed. Firstly, the selected multiscale feature layer is merged with the scale-invariant convolutional layer through the feature pyramid network structure; at the same time, the multiscale feature map is separately converted into the channel number using the scale-invariant convolution kernel. Then, the obtained two sets of pyramid-shaped feature layers are further feature fused to generate a set of enhanced multiscale feature maps, and the scale-invariant convolution is performed again on these layers. Finally, the obtained layer is used for detection and localization. The final location coordinates and confidence are output after nonmaximum suppression. Experimental results on the Pascal VOC 2007 and 2012 datasets confirm that there is a 8.2% improvement in mAP compared to the original SSD and some existing algorithms.

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Selective Refinement Network for High Performance Face Detection

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33018231 ◽

2019 ◽

Vol 33 ◽

pp. 8231-8238 ◽

Cited By ~ 20

Author(s):

Cheng Chi ◽

Shifeng Zhang ◽

Junliang Xing ◽

Zhen Lei ◽

Stan Z. Li ◽

...

Keyword(s):

Receptive Field ◽

Face Detection ◽

High Performance ◽

Field Enhancement ◽

Search Space ◽

Single Shot ◽

The Face ◽

Face Datasets ◽

High Level ◽

Face Detector

High performance face detection remains a very challenging problem, especially when there exists many tiny faces. This paper presents a novel single-shot face detector, named Selective Refinement Network (SRN), which introduces novel twostep classification and regression operations selectively into an anchor-based face detector to reduce false positives and improve location accuracy simultaneously. In particular, the SRN consists of two modules: the Selective Two-step Classification (STC) module and the Selective Two-step Regression (STR) module. The STC aims to filter out most simple negative anchors from low level detection layers to reduce the search space for the subsequent classifier, while the STR is designed to coarsely adjust the locations and sizes of anchors from high level detection layers to provide better initialization for the subsequent regressor. Moreover, we design a Receptive Field Enhancement (RFE) block to provide more diverse receptive field, which helps to better capture faces in some extreme poses. As a consequence, the proposed SRN detector achieves state-of-the-art performance on all the widely used face detection benchmarks, including AFW, PASCAL face, FDDB, and WIDER FACE datasets. Codes will be released to facilitate further studies on the face detection problem.

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