scholarly journals SE-IYOLOV3: An Accurate Small Scale Face Detector for Outdoor Security

Mathematics ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 93 ◽  
Author(s):  
Zhenrong Deng ◽  
Rui Yang ◽  
Rushi Lan ◽  
Zhenbing Liu ◽  
Xiaonan Luo
Keyword(s):  
Receptive Field ◽  
Face Detection ◽  
Network Structure ◽  
Difficult Problem ◽  
Detection Performance ◽  
Experimental Results ◽  
Small Scale ◽  
Detection Accuracy ◽  
Novel Method ◽  
Face Detector

Small scale face detection is a very difficult problem. In order to achieve a higher detection accuracy, we propose a novel method, termed SE-IYOLOV3, for small scale face in this work. In SE-IYOLOV3, we improve the YOLOV3 first, in which the anchorage box with a higher average intersection ratio is obtained by combining niche technology on the basis of the k-means algorithm. An upsampling scale is added to form a face network structure that is suitable for detecting dense small scale faces. The number of prediction boxes is five times more than the YOLOV3 network. To further improve the detection performance, we adopt the SENet structure to enhance the global receptive field of the network. The experimental results on the WIDERFACEdataset show that the IYOLOV3 network embedded in the SENet structure can significantly improve the detection accuracy of dense small scale faces.

scholarly journals Small-Scale Face Detection Based on Improved R-FCN

2020 ◽  
Vol 10 (12) ◽  
pp. 4177
Author(s):  
Chaowei Tang ◽  
Shiyu Chen ◽  
Xu Zhou ◽  
Shuai Ruan ◽  
Haotian Wen
Keyword(s):  
Receptive Field ◽  
Face Detection ◽  
Feature Fusion ◽  
Selection Method ◽  
Local Information ◽  
Small Scale ◽  
Average Precision ◽  
Convolutional Network ◽  
High Layer ◽  
Novel Method

Face detection is an important basic technique for face-related applications, such as face analysis, recognition, and reconstruction. Images in unconstrained scenes may contain many small-scale faces. The features that the detector can extract from small-scale faces are limited, which will cause missed detection and greatly reduce the precision of face detection. Therefore, this study proposes a novel method to detect small-scale faces based on region-based fully convolutional network (R-FCN). First, we propose a novel R-FCN framework with the ability of feature fusion and receptive field adaptation. Second, a bottom-up feature fusion branch is established to enrich the local information of high-layer features. Third, a receptive field adaptation block (RFAB) is proposed to ensure that the receptive field can be adaptively selected to strengthen the expression ability of features. Finally, we improve the anchor setting method and adopt soft non-maximum suppression (SoftNMS) as the selection method of candidate boxes. Experimental results show that average precision for small-scale face detection of R-FCN with feature fusion branch and RFAB (RFAB-f-R-FCN) is improved by 0.8%, 2.9%, and 11% on three subsets of Wider Face compared with that of R-FCN.

Fast Face Detection Based on AdaBoost and Canny Operators

2014 ◽  
Vol 716-717 ◽  
pp. 936-939
Author(s):  
Lin Zhang
Keyword(s):  
Face Detection ◽  
Detection Method ◽  
Face Image ◽  
Experimental Results ◽  
Detection Accuracy ◽  
Human Face ◽  
Adaboost Algorithm ◽  
Amount Of Information ◽  
Detection Speed

Detection speed of traditional face detection method based on AdaBoost algorithm is slow since AdaBoost asks a large number of features. Therefore, to address this shortcoming, we proposed a fast face detection method based on AdaBoost and canny operators in this paper. Firstly, we use canny operators to detect edge of face image which separates the region of the possible human face from image, and then do face detection in the separated region using Modest AdaBoost algorithm (MAB). Before using MAB to achieve face detection, utilizing canny operators to detect edge can make this algorithm effectively filter information, retain useful information, reduce the amount of information and improve detection speed. Experimental results show that the algorithm can obtain higher detection accuracy and detection speed has been significantly improved at the same time.

Mask Wearing Detection Algorithm Based on Improved Tiny YOLOv3

2021 ◽  
pp. 2155007
Author(s):  
Guohua Liu ◽  
Qintao Zhang
Keyword(s):  
Loss Function ◽  
Network Structure ◽  
High Speed ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Detection Accuracy ◽  
Bounding Box

The new coronavirus spreads widely through droplets, aerosols and other carriers. Wearing a mask can effectively reduce the probability of being infected by the virus. Therefore, it is necessary to monitor whether people wear masks in public to prevent the virus from spreading further. However, there is no mature general mask wearing detection algorithm. Based on tiny YOLOv3 algorithm, this paper realizes the detection of face with mask and face without mask, and proposes an improvement to the algorithm. First, the loss function of the bounding box regression is optimized, and the original loss function is optimized as the Generalized Intersection over Union (GIoU) loss. Second, the network structure is improved, the residual unit is introduced into the backbone to increase the depth of the network and the detection of two scales is expanded to three. Finally, the size of anchor boxes is clustered based on [Formula: see text]-means algorithm. The experimental results on the constructed dataset show that, compared with the tiny YOLOv3 algorithm, the algorithm proposed in this paper improves the detection accuracy while maintaining high-speed inference ability.

scholarly journals A Support Vector Data Description Committee for Face Detection

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yi-Hung Liu ◽  
Yung Ting ◽  
Shian-Shing Shyu ◽  
Chang-Kuo Chen ◽  
Chung-Lin Lee ◽  
...  
Keyword(s):  
Face Detection ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Detection Accuracy ◽  
Vector Data ◽  
Data Set ◽  
Data Description ◽  
The Face ◽  
Face Detector

Face detection is a crucial prestage for face recognition and is often treated as a binary (face and nonface) classification problem. While this strategy is simple to implement, face detection accuracy would drop when nonface training patterns are undersampled. To avoid these problems, we propose in this paper a one-class learning-based face detector called support vector data description (SVDD) committee, which consists of several SVDD members, each of which is trained on a subset of face patterns. Nonfaces are not required in the training of the SVDD committee. Therefore, the face detection accuracy of SVDD committee is independent of the nonface training patterns. Moreover, the proposed SVDD committee is also able to improve generalization ability of the original SVDD when the face data set has a multicluster distribution. Experiments carried out on the extended MIT face data set show that the proposed SVDD committee can achieve better face detection accuracy than the widely used SVM face detector and performs better than other one-class classifiers, including the original SVDD and the kernel principal component analysis (Kernel PCA).

Rapid Human Finding with Motion Segmentation for Mobile Robot

2017 ◽  
Vol 32 (04) ◽  
pp. 1855004
Author(s):  
Yutong Gao ◽  
Weimin Lei ◽  
Xie Xie ◽  
Yue Fu ◽  
Lu Zhang
Keyword(s):  
Mobile Robot ◽  
Face Detection ◽  
Real Life ◽  
Detection Accuracy ◽  
Fisher Discriminant Analysis ◽  
Adaboost Algorithm ◽  
Fast Detection ◽  
Fisher Discriminant ◽  
Flow Algorithm ◽  
Face Detector

A computer vision method is presented for the mobile robot to find humans in scene. Face detection is used for confirming humans. In order to reduce regions of search, optical flow algorithm is used to segment the image in advance. Asymmetric problems in face detection are explained, and relative solutions are put forward by bootstrapping strategy and asymmetric adaboost algorithm. In addition, fisher discriminant analysis further improves the performance of face detection. Multi-view face models are trained to accommodate practical face detection application. At last, experiments demonstrate that our multi-view face detector achieves high detection accuracy and fast detection speed on both standard testing datasets and real-life images.

A Novel Method for Grinding Wheel Setting Based on Acoustic Emissions

2016 ◽  
Vol 874 ◽  
pp. 79-84 ◽  
Author(s):  
Xiang Long Zhu ◽  
Zhen Hua Jiao ◽  
Ren Ke Kang ◽  
Zi Guang Wang ◽  
Hui Xu
Keyword(s):  
Acoustic Emission ◽  
Negative Impact ◽  
Acoustic Emissions ◽  
Experimental Results ◽  
Grinding Wheel ◽  
Detection Accuracy ◽  
Grinding Process ◽  
Novel Method ◽  
Ae Signals ◽  
Better Than

Wheel setting is difficult in a grinding process and may directly apply a negative impact on grinding accuracy and efficiency. This study presents a novel method for grinding wheel setting based on acoustic emissions. The method experimentally detects the acoustic emission (AE) signals that come from the touch-down of the grinding wheel with the workpiece. The experimental results show that the measured AE signals monotonically increase with grinding depth and can be used for detection of wheel setting in a grinding process with a detection accuracy better than 0.5μm.

scholarly journals Selective Refinement Network for High Performance Face Detection

2019 ◽  
Vol 33 ◽  
pp. 8231-8238 ◽  
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.

scholarly journals KPNet: Towards Minimal Face Detector

2020 ◽  
Vol 34 (07) ◽  
pp. 12015-12022
Author(s):  
Guanglu Song ◽  
Yu Liu ◽  
Yuhang Zang ◽  
Xiaogang Wang ◽  
Biao Leng ◽  
...  
Keyword(s):  
Receptive Field ◽  
Face Detection ◽  
State Of The Art ◽  
Fine Grained ◽  
Front End ◽  
The Face ◽  
Bounding Boxes ◽  
Definition Of ◽  
Appearance Feature ◽  
Face Detector

The small receptive field and capacity of minimal neural networks limit their performance when using them to be the backbone of detectors. In this work, we find that the appearance feature of a generic face is discriminative enough for a tiny and shallow neural network to verify from the background. And the essential barriers behind us are 1) the vague definition of the face bounding box and 2) tricky design of anchor-boxes or receptive field. Unlike most top-down methods for joint face detection and alignment, the proposed KPNet detects small facial keypoints instead of the whole face by in the bottom-up manner. It first predicts the facial landmarks from a low-resolution image via the well-designed fine-grained scale approximation and scale adaptive soft-argmax operator. Finally, the precise face bounding boxes, no matter how we define it, can be inferred from the keypoints. Without any complex head architecture or meticulous network designing, the KPNet achieves state-of-the-art accuracy on generic face detection and alignment benchmarks with only ∼ 1M parameters, which runs at 1000fps on GPU and is easy to perform real-time on most modern front-end chips.

scholarly journals Mask-Refined R-CNN: A Network for Refining Object Details in Instance Segmentation

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1010 ◽  
Author(s):  
Yiqing Zhang ◽  
Jun Chu ◽  
Lu Leng ◽  
Jun Miao
Keyword(s):  
Receptive Field ◽  
Spatial Information ◽  
Feature Fusion ◽  
State Of The Art ◽  
Rapid Development ◽  
Experimental Results ◽  
Small Scale ◽  
Feature Maps ◽  
Segmentation Accuracy ◽  
Instance Segmentation

With the rapid development of flexible vision sensors and visual sensor networks, computer vision tasks, such as object detection and tracking, are entering a new phase. Accordingly, the more challenging comprehensive task, including instance segmentation, can develop rapidly. Most state-of-the-art network frameworks, for instance, segmentation, are based on Mask R-CNN (mask region-convolutional neural network). However, the experimental results confirm that Mask R-CNN does not always successfully predict instance details. The scale-invariant fully convolutional network structure of Mask R-CNN ignores the difference in spatial information between receptive fields of different sizes. A large-scale receptive field focuses more on detailed information, whereas a small-scale receptive field focuses more on semantic information. So the network cannot consider the relationship between the pixels at the object edge, and these pixels will be misclassified. To overcome this problem, Mask-Refined R-CNN (MR R-CNN) is proposed, in which the stride of ROIAlign (region of interest align) is adjusted. In addition, the original fully convolutional layer is replaced with a new semantic segmentation layer that realizes feature fusion by constructing a feature pyramid network and summing the forward and backward transmissions of feature maps of the same resolution. The segmentation accuracy is substantially improved by combining the feature layers that focus on the global and detailed information. The experimental results on the COCO (Common Objects in Context) and Cityscapes datasets demonstrate that the segmentation accuracy of MR R-CNN is about 2% higher than that of Mask R-CNN using the same backbone. The average precision of large instances reaches 56.6%, which is higher than those of all state-of-the-art methods. In addition, the proposed method requires low time cost and is easily implemented. The experiments on the Cityscapes dataset also prove that the proposed method has great generalization ability.

scholarly journals SACN: A Novel Rotating Face Detector Based on Architecture Search

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 558
Author(s):  
Anping Song ◽  
Xiaokang Xu ◽  
Xinyi Zhai
Keyword(s):  
Face Detection ◽  
Human Face ◽  
Angle Error ◽  
Rotation Invariant ◽  
Convolutional Network ◽  
Data Set ◽  
Practical Applications ◽  
Model Size ◽  
Average Angle ◽  
Face Detector

Rotation-Invariant Face Detection (RIPD) has been widely used in practical applications; however, the problem of the adjusting of the rotation-in-plane (RIP) angle of the human face still remains. Recently, several methods based on neural networks have been proposed to solve the RIP angle problem. However, these methods have various limitations, including low detecting speed, model size, and detecting accuracy. To solve the aforementioned problems, we propose a new network, called the Searching Architecture Calibration Network (SACN), which utilizes architecture search, fully convolutional network (FCN) and bounding box center cluster (CC). SACN was tested on the challenging Multi-Oriented Face Detection Data Set and Benchmark (MOFDDB) and achieved a higher detecting accuracy and almost the same speed as existing detectors. Moreover, the average angle error is optimized from the current 12.6° to 10.5°.

Sign in / Sign up

Export Citation Format

Share Document