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Automated Video-Based Face detection Using Harris Hawks Optimization with Deep Learning

Computers, Materials & Continua 2023年第6期75卷 4917-4933页

作者： Latifah Almuqren Manar Ahmed Hamza Abdullah Mohamed Amgad Atta Abdelmageed Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt

face recognition technology automatically identifies an individual from image or video sources.The detection process can be done by attaining facial characteristics from the image of a subject face.Recent developments in deep learning(DL)and computer vision(CV)techniques enable the design of automated face recognition and tracking methods.This study presents a novel Harris Hawks Optimization with deep learning-empowered automated Face detection and tracking(HHODL-AFDT)method.The proposed HHODL-AFDT model involves a Faster region based convolution neural network(RCNN)-based Face detection model and HHO-based hyperparameter opti-mization process.The presented optimal Faster RCNN model precisely rec-ognizes the face and is passed into the face-tracking model using a regression network(REGN).The face tracking using the REGN model uses the fea-tures from neighboring frames and foresees the location of the target face in succeeding frames.The application of the HHO algorithm for optimal hyperparameter selection shows the novelty of the work.The experimental validation of the presented HHODL-AFDT algorithm is conducted using two datasets and the experiment outcomes highlighted the superior performance of the HHODL-AFDT model over current methodologies with maximum accuracy of 90.60%and 88.08%under PICS and VTB datasets,respectively.

关键词： Face detection face tracking deep learning computer vision video surveillance parameter tuning

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Secure Rotation Invariant Face detection System for Authentication

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Computers, Materials & Continua 2022年第1期70卷 1955-1974页

作者： Amit Verma Mohammed Baljon Shailendra Mishra Iqbaldeep Kaur Ritika Saini Sharad Saxena Sanjay Kumar Sharma Department of Computer Science&Engineering Chandigarh Group of CollegesMohali140317India Department of Computer Engineering College of Computer&Information ScienceMajmaah University11952Saudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering&TechnologyPatiala147004India Department of Computer Centre&Campus Network Facility Science Complex PO Central UniversityGachibowliHyderabad500046TelanganaIndia

Biometric applications widely use the face as a component for recognition and automatic detection.face rotation is a variable component and makes Face detection a complex and challenging task with varied angles and rotation.This problem has been investigated,and a novice algorithm,namely RIFDS(Rotation Invariant Face detection System),has been devised.The objective of the paper is to implement a robust method for Face detection taken at various angle.Further to achieve better results than known algorithms for Face detection.In RIFDS Polar Harmonic Transforms(PHT)technique is combined with Multi-Block Local Binary Pattern(MBLBP)in a hybrid manner.The MBLBP is used to extract texture patterns from the digital image,and the PHT is used to manage invariant rotation characteristics.In this manner,RIFDS can detect human faces at different rotations and with different facial expressions.The RIFDS performance is validated on different face databases like LFW,ORL,CMU,MIT-CBCL,JAFFF face Databases,and Lena images.The results show that the RIFDS algorithm can detect faces at varying angles and at different image resolutions and with an accuracy of 99.9%.The RIFDS algorithm outperforms previous methods like Viola-Jones,Multi-blockLocal Binary Pattern(MBLBP),and Polar HarmonicTransforms(PHTs).The RIFDS approach has a further scope with a genetic algorithm to detect faces(approximation)even from shadows.

关键词： Pose variations Face detection frontal faces facial expressions emotions

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End-to-end spatial transform Face detection and recognition

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Virtual Reality & Intelligent Hardware 2020年第2期2卷 119-131页

作者： Hongxin ZHANG Liying CHI CAD&CG State Key Lab Zhejiang UniversityHangzhou 310058China

Background Several Face detection and recogni tion methods have been proposed in the past decades that have excellent performance.The conventional face recognition pipeline comprises the following:(1)Face detection,(2)face alignment,(3)feature extraction,and(4)similarity,which are independent of each other.The separate facial analysis stages lead to redundant model calculations,and are difficult for use in end-to-end training.Methods In this paper,we propose a novel end-to-end trainable convolutional network framework for Face detection and recognition,in which a geometric transformation matrix is directly learned to align the faces rather than predicting the facial landmarks.In the training stage,our single CNN model is supervised only by face bounding boxes and personal identities,which are publicly available from WIDER face and CASIA-Webface datasets.Our model is tested on Face detection Dataset and Benchmark(FDDB)and Labeled face in the Wild(LFW)datasets.Results The results show 89.24%recall for Face detection tasks and 98.63%accura cy for face recognition tasks.

关键词： Face detection face recognition Spatial transform Feature fusion

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Face detection And Recognition In Complex Environments

Face Detection And Recognition In Complex Environments

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第40届中国控制会议

作者： Haopeng Wu Zhiying Lu Jianlin Guo Tengwei Ren School of Electrical and Information Engineering Tianjin University

With the development of deep learning, the perception of facial image has wide prospects of application.In a crowded environment, there is a problem that faces cannot be detected due to mutual occlusion of faces, and the low resolution causes the face to be blurred. In order to detect the missing faces which were occluded in the dense environments, a kind of model, which based on multi-task cascaded convolutional network, was put forth. We improve the detection scheme by using Soft non-maximum suppression algorithm.Aiming at the problem of faces with proportion of inconsistent caused by the inconsistency of the distance between the face and the camera device, this paper extracts facial features based on the deep res-net. For high and low resolution facial images, a dual-branch extraction feature network is used to make images of different resolutions are clustered in a public feature space. At the same time, the central loss function, parameterized rectification linear unit and batch normalization are used to improve the network training process, which improves the network’s feature classification performance and the convergence speed in the training process. WIDER face database is used to verify the Face detection method. LFW database and YALE face database are used to verify face recognition method. Experiments show that the network proposed in this paper has good robustness against occlusion and low resolution in Face detection, and effectively expands the distance between classes in face recognition and improves the recognition accuracy.

关键词： face recognition Face detection Super-Resolution Deep learning

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An Improved Mobilenet-SSD Approach For Face detection

An Improved Mobilenet-SSD Approach For Face Detection

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第40届中国控制会议

作者： Jin Tang Xiwei Peng Xin Chen Bai Luo School of Automation Beijing Institute of Technology

Mobilenet-SSD is a lightweight network with high efficiency, which is widely used in the field of real-time Face detection. Whereas, it fails to achieve similar high performances compared to region-based CNN methods. In this paper, we propose an improved Mobilenet-SSD approach by optimizing the feature map and the number of prior boxes of the original Mobilenet-SSD. These changes permit the proposed approach to get a high precision and recall in Face detection. To assist further with Face detection, the method of non-maximum suppression is employed to remove redundant candidate boxes. To evaluate the proposed method, we conduct experiments on the well-known FDDB benchmark dataset. For 300 × 300 input, the proposed method achieves 91.92% average precision（AP） at 39.0 frames per second（FPS） on GEFORCE GTX 1650. Throughout experimental results, we demonstrate that our approach achieves a considerable improvement on the AP with a slightly degraded speed compared with Mobilenet-SSD. In addition, our approach also outperforms Single Shot MultiBox Detector（SSD） in terms of speed and model size.

关键词： Face detection Mobilenet-SSD Depthwise separable convolution

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Real-Time Multi-View Face detection and Pose Estimation Based on Cost-Sensitive AdaBoost

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Tsinghua Science and Technology 2005年第2期10卷 152-157页

作者：马勇丁晓青 Department of Electronic Engineering Tsinghua University Beijing 100084 China

Locating multi-view faces in images with a complex background remains a challenging problem. In this paper, an integrated method for real-time multi-view Face detection and pose estimation is presented. A simple-to-complex and coarse-to-fine view-based detector architecture has been designed to detect multi- view faces and estimate their poses efficiently. Both the pose estimators and the view-based face/nonface detectors are trained by a cost-sensitive AdaBoost algorithm to improve the generalization ability. Experi- mental results show that the proposed multi-view face detector, which can be constructed easily, gives more robust Face detection and pose estimation and has a faster real-time detection speed compared with other conventional methods.

关键词： Face detection pose estimation multi-view Face detection AdaBoost

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Fast Face detection with Multi-Scale Window Search Free from Image Resizing Using SGI Features

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Journal of Computer and Communications 2016年第10期4卷 22-29页

作者： Masayuki Miyama Faculty of Electrical and Computer Engineering Institute of Science and Engineering Kanazawa University Kanazawa Japan

Face detection is applied to many tasks such as auto focus control, surveillance, user interface, and face recognition. Processing speed and detection accuracy of the Face detection have been improved continuously. This paper describes a novel method of fast Face detection with multi-scale window search free from image resizing. We adopt statistics of gradient images (SGI) as image features and append an overlapping cell array to improve detection accuracy. The SGI feature is scale invariant and insensitive to small difference of pixel value. These characteristics enable the multi-scale window search without image resizing. Experimental results show that processing speed of our method is 3.66 times faster than a conventional method, adopting HOG features combined to an SVM classifier, without accuracy degradation.

关键词： Face detection Multi-Scale Window Search Resizing Free SGI Feature

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Research and Implementation of Face detection System on Android Smart Phone

Research and Implementation of Face Detection System on Andr...

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2013年中国智能自动化学术会议

作者： Xin Li Yumei Zhai Xiong Li College of Information Science and Engineering Guilin University of Technology Guangxi Key Laboratory of Spatial Information and Geomatics Guilin University of Technology

In order to improve the effect of detecting faces in Android smart phone,we proposed an effective Face detection algorithm.Cluster analysis is used to segment skin region from the input color image in YCbCr color space.After the normalization of candidate regions being finished,the weighted Euclid distance is calculated between the reference templates and candidate regions.Based on this algorithm we implemented a Face detection system on Android platform.Experiment proves that the system allows robust and has a high detection rate.

关键词： Face detection Android Cluster analysis Template matching

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An Improved detection Algorithm of face with Combining AdaBoost and SVM 25th

An Improved Detection Algorithm of Face with Combining AdaBo...

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第25届中国控制与决策会议

作者： Jia Mingxing Du Junqiang Cheng Tao Yang Ning Jiang Yi Zhang Zhen Academy of Information Science and Engineer Northeastern University

ISBN: (纸本)9781467355339

This paper proposes a Face detection method by combining SVM into the traditional Face detection algorithm named AdaBoost. This method adopts SVM classifier instead of traditional AdaBoost classifier based on AdaBoost cascade framework. To get SVM classifier, extract several Haar-like features with the strongest classification ability in the process of training SVM classifier. To ensure the generalization capability of classifier classify validation set, and using the penalty factor setting with focus makes SVM classifier pay more attention to the face samples, while the training samples reached a high goodness of fit. At the same time, this thesis improves the extraction method of Haar-like feature. Finally, using matlab simulation test the effectiveness of the algorithm. The experiments show that the improved detection algorithm of face with combining AdaBoost and SVM reduces the amount of computation and improve the detection rate.

关键词： AdsBoost algorithm Face detection Haar-like feature SVM

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Using Skin Color and HAD-AdaBoost Algorithm for Face detection in Color Images

Using Skin Color and HAD-AdaBoost Algorithm for Face Detecti...

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2012 National Conference on Information Technology and Computer Science

作者： Li-Jie Xue School of Electronic and Information Engineering,Jiangsu University Zhenjiang,PR of China Zheng-Ming Li School of Electronic and Information Engineering,Jiangsu University Zhenjiang,PR of China

Owing to the interference of the complex background in color image,high false positive rate is a problem in Face detection based on AdaBoost algorithm.In addition,the training process of AdaBoost is very time consuming.To address these problems,this paper proposes a two-stage Face detection method using skin color segmentation and heuristics-structured adaptive to detection AdaBoost （HAD-AdaBoost） algorithm.Firstly,skin color segmentation is applied to remove most of the background.Then,the HAD-AdaBoost cascade classifier is performed on the candidates to make a final face location.By introducing the algorithm of adaptive to detection AdaBoost algorithm （AD AdaBoost） and heuristicsstructured cascade to our system,the resulting classifier consisting of fewer weak classifiers achieves lower error rates.The experimental results demonstrate that our system retrieves 90.24% of the detection rate with 1.69% false alarms on the color image set.

关键词： Face detection skin color segmentation AD AdaBoost heuristics-structured cascade

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