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Hard-Mining Loss based Convolutional Neural Network for Face Recognition [article]

Yash Srivastava and Vaishnav Murali and Shiv Ram Dubey
<span title="2020-12-23">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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<span title="2019-08-09">2019</span> <span class="release-stage" >pre-print</span> <span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1908.09747v1">arXiv:1908.09747v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/h6xtqvgvtbfkrohwepqshbihc4">fatcat:h6xtqvgvtbfkrohwepqshbihc4</a> </span>
Witnessing advances in deep learning, significant work has been observed in face recognition, which touched upon various parts of the recognition framework like Convolutional Neural Network (CNN), Layers  ...  Various loss functions such as Cross-Entropy, Angular-Softmax and ArcFace have been introduced to learn the weights of network for face recognition.  ...  The stochastic gradient descent (SGD) optimization is widely adapted to train the Convolutional Neural Networks (CNNs).  ... 
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Citation

Yash Srivastava and Vaishnav Murali and Shiv Ram Dubey. "Hard-Mining Loss based Convolutional Neural Network for Face Recognition." arXiv (2020)

Doppelganger Mining for Face Representation Learning

Evgeny Smirnov, Aleksandr Melnikov, Sergey Novoselov, Eugene Luckyanets, Galina Lavrentyeva
<span title="">2017</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/6s36fqp6q5hgpdq2scjq3sfu6a" style="color: black;">2017 IEEE International Conference on Computer Vision Workshops (ICCVW)</a> </i> &nbsp;

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It is especially useful for methods, based on exemplar-based supervision.  ...  Usually hard example mining comes with a price of necessity to use large mini-batches or substantial extra computation and memory cost, particularly for datasets with large numbers of identities.  ...  In this paper we propose Doppelganger mining -a new simple sampling method, which improves the training of Deep Convolutional Neural Networks for face recognition.  ... 
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Citation

Evgeny Smirnov, Aleksandr Melnikov, Sergey Novoselov, Eugene Luckyanets, Galina Lavrentyeva. "Doppelganger Mining for Face Representation Learning." 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) (2017) 1916-1923

Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks

Kaipeng Zhang, Zhanpeng Zhang, Zhifeng Li, Yu Qiao
<span title="">2016</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/msfmoh6v7bdk7lrsmtbklto74i" style="color: black;">IEEE Signal Processing Letters</a> </i> &nbsp;

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In particular, our framework adopts a cascaded structure with three stages of carefully designed deep convolutional networks that predict face and landmark location in a coarse-to-fine manner.  ...  Our method achieves superior accuracy over the state-of-the-art techniques on the challenging FDDB and WIDER FACE benchmark for face detection, and AFLW benchmark for face alignment, while keeps real time  ...  [11] train deep convolution neural networks for facial attribute recognition to obtain high response in face regions which further yield candidate windows of faces.  ... 
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Citation

Kaipeng Zhang, Zhanpeng Zhang, Zhifeng Li, Yu Qiao. "Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks." IEEE Signal Processing Letters 23.10 (2016) 1499-1503

Hard Example Mining with Auxiliary Embeddings

Evgeny Smirnov, Elizaveta Ivanova, Aleksandr Melnikov, Ilya Kalinovskiy, Andrei Oleinik, Eugene Luckyanets
<span title="">2018</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ilwxppn4d5hizekyd3ndvy2mii" style="color: black;">2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</a> </i> &nbsp;

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Our experiments on the challenging Disguised Faces in the Wild (DFW) dataset show that hard example mining with auxiliary embeddings improves the discriminative power of learned representations.  ...  With the help of these embeddings it is possible to select new examples for the mini-batch based on their similarity with the already selected examples.  ...  In the context of face recognition, it can be achieved by means of a pre-trained attribute classification neural network.  ... 
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Evgeny Smirnov, Elizaveta Ivanova, Aleksandr Melnikov, Ilya Kalinovskiy, Andrei Oleinik, Eugene Luckyanets. "Hard Example Mining with Auxiliary Embeddings." 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2018) 37-46

Metric Classification Network in Actual Face Recognition Scene [article]

Jian Li, Yan Wang, Xiubao Zhang, Weihong Deng, Haifeng Shen
<span title="2019-10-25">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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These experiments confirm the effectiveness of validation classifier on face recognition task.  ...  This is very inappropriate for application in real-world scenarios.  ...  , followed by a similarity measure also based on neural network.  ... 
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Jian Li, Yan Wang, Xiubao Zhang, Weihong Deng, Haifeng Shen. "Metric Classification Network in Actual Face Recognition Scene." arXiv (2019)

MassFace: an efficient implementation using triplet loss for face recognition [article]

Yule Li
<span title="2019-02-28">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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In this paper we present an efficient implementation using triplet loss for face recognition. We conduct the practical experiment to analyze the factors that influence the training of triplet loss.  ...  We analyze the experiment results and give some insights to help others balance the factors when they apply triplet loss to their own problem especially for face recognition task.  ...  Introduction Face recognition has achieved significant improvement due to the power of deep representation through convolutional neural network.  ... 
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Yule Li. "MassFace: an efficient implementation using triplet loss for face recognition." arXiv (2019)

Detection of Pin Defects in Aerial Images Based on Cascaded Convolutional Neural Network

Yewei Xiao, Zhiqiang Li, Dongbo Zhang, Lianwei Teng
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;

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INDEX TERM pin defect, aerial image, cascaded convolutional neural network, nonlinear multilayer perceptron, hard sample mining  ...  This paper proposed a target detection method based on cascaded convolutional neural networks.  ...  For pre-training, we adopt the strategy of online hard sample mining (OHEM) [23] .  ... 
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Yewei Xiao, Zhiqiang Li, Dongbo Zhang, Lianwei Teng. "Detection of Pin Defects in Aerial Images Based on Cascaded Convolutional Neural Network." IEEE Access (2021) 1-1

Real-Time Face Recognition System for Remote Employee Tracking [article]

Mohammad Sabik Irbaz, MD Abdullah Al Nasim, Refat E Ferdous
<span title="2021-10-13">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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<span title="2021-07-15">2021</span> <span class="release-stage" >pre-print</span>
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To deal with the challenge effectively, we came up with a solution to track the employees with face recognition. We have been testing this system experimentally for our office.  ...  To train the face recognition module, we used FaceNet with KNN using the Labeled Faces in the Wild (LFW) dataset and achieved 97.8\% accuracy.  ...  For identifying faces two approaches are explained here. First one is effective Convolutional Neural Network designs for biometric face recognition.  ... 
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Mohammad Sabik Irbaz, MD Abdullah Al Nasim, Refat E Ferdous. "Real-Time Face Recognition System for Remote Employee Tracking." arXiv (2021)

Improved YOLOv3 Object Classification in Intelligent Transportation System [article]

Yang Zhang, Changhui Hu, Xiaobo Lu
<span title="2020-04-08">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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The proposed model and contrast experiment are conducted on our self-build traffic driver's face database.  ...  In this paper, an algorithm based on YOLOv3 is proposed to realize the detection and classification of vehicles, drivers, and people on the highway, so as to achieve the purpose of distinguishing driver  ...  [7] proposed the deep neural networks for facial feature recognition, aiming at getting high reply in face regions, resulting in producing candidate windows of human faces.  ... 
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Yang Zhang, Changhui Hu, Xiaobo Lu. "Improved YOLOv3 Object Classification in Intelligent Transportation System." arXiv (2020)

LANDMARK-BASED VISUAL PLACE RECOGNITION

Swapnali Gavali, Dr. Bashirahamad Momin
<span title="2021-03-01">2021</span> <i title="IJEAST"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/namhphsg6rdvtofp27o3scimoy" style="color: black;">International Journal of Engineering Applied Sciences and Technology</a> </i> &nbsp;

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By fine-tuning pre-trained convolutional neural network (CNN) and minimizing triplet loss, the triplet network can learn appropriate metrics so that most similar images can be retrieved through algorithms  ...  This paper presents the application of a triplet network for large scale landmarkbased visual place recognition.  ...  [7] In this study, examined Siamese convolutional neural network architectures to verify authorship of handwritten text.  ... 
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<span class="ui label basic orange small" title="Open Access">Open Access</span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211017085636/https://ijeast.com/papers/217-221,Tesma511,IJEAST.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/8c/c1/8cc17c37d085f3c8a6e94bac7a8cebef464584bf.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.33564/ijeast.2021.v05i11.035"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>
Citation

Swapnali Gavali, Dr. Bashirahamad Momin. "LANDMARK-BASED VISUAL PLACE RECOGNITION." International Journal of Engineering Applied Sciences and Technology 5.11 (2021)

Bootstrapping Face Detection with Hard Negative Examples [article]

Shaohua Wan, Zhijun Chen, Tao Zhang, Bo Zhang, Kong-kat Wong
<span title="2016-08-07">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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The key is to exploit the idea of hard negative mining and iteratively update the Faster R-CNN based face detector with the hard negatives harvested from a large set of background examples.  ...  Recently significant performance improvement in face detection was made possible by deeply trained convolutional networks.  ...  Since the remarkable success of the deep Convolutional Neural Network (CNN) [10] in image classification on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012, numerous efforts have  ... 
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Shaohua Wan, Zhijun Chen, Tao Zhang, Bo Zhang, Kong-kat Wong. "Bootstrapping Face Detection with Hard Negative Examples." arXiv (2016)

TinaFace: Strong but Simple Baseline for Face Detection [article]

Yanjia Zhu, Hongxiang Cai, Shuhan Zhang, Chenhao Wang, Yichao Xiong
<span title="2021-01-22">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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<span title="2020-12-02">2020</span> <span class="release-stage" >pre-print</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.13183v2">arXiv:2011.13183v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7eul23xuibct7lczlpajqsehcq">fatcat:7eul23xuibct7lczlpajqsehcq</a> </span>
<span title="2020-11-26">2020</span> <span class="release-stage" >pre-print</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.13183v1">arXiv:2011.13183v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/orxhv6llsjesnklzcmqr4jubuy">fatcat:orxhv6llsjesnklzcmqr4jubuy</a> </span>
On the hard test set of the most popular and challenging face detection benchmark WIDER FACE , with single-model and single-scale, our TinaFace achieves 92.1% average precision (AP), which exceeds most  ...  Many works present lots of special methods for face detection from different perspectives like model architecture, data augmentation, label assignment and etc., which make the overall algorithm and system  ...  “Face detection through scale- over union: A metric and a loss for bounding box friendly deep convolutional networks”. In: arXiv regression”.  ... 
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Citation

Yanjia Zhu, Hongxiang Cai, Shuhan Zhang, Chenhao Wang, Yichao Xiong. "TinaFace: Strong but Simple Baseline for Face Detection." arXiv (2021)

Are Gabor Kernels Optimal for Iris Recognition? [article]

Aidan Boyd, Adam Czajka, Kevin Bowyer
<span title="2020-02-20">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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We use (on purpose) a single-layer convolutional neural network as it mimics an iris code-based algorithm.  ...  Gabor kernels are widely accepted as dominant filters for iris recognition.  ...  We propose to use a single-layer convolutional neural network that replicates a Daugman's approach to iris recognition.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2002.08959v1">arXiv:2002.08959v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/frrssua4zzbdlh7kbbpr5ppzgu">fatcat:frrssua4zzbdlh7kbbpr5ppzgu</a> </span>
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Citation

Aidan Boyd, Adam Czajka, Kevin Bowyer. "Are Gabor Kernels Optimal for Iris Recognition?." arXiv (2020)

DenseBox: Unifying Landmark Localization with End to End Object Detection [article]

Lichao Huang and Yi Yang and Yafeng Deng and Yinan Yu
<span title="2015-09-19">2015</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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How can a single fully convolutional neural network (FCN) perform on object detection?  ...  We present experimental results on public benchmark datasets including MALF face detection and KITTI car detection, that indicate our DenseBox is the state-of-the-art system for detecting challenging objects  ...  [10] , is a face detection system based on convolutional neural networks.  ... 
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<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200902045358/https://arxiv.org/pdf/1509.04874v3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/2e/26/2e268598d9c2fd9757ba43f7967e57b8a2a871f4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1509.04874v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>
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Lichao Huang and Yi Yang and Yafeng Deng and Yinan Yu. "DenseBox: Unifying Landmark Localization with End to End Object Detection." arXiv (2015)

Seeing What is Not There: Learning Context to Determine Where Objects are Missing

Jin Sun, David W. Jacobs
<span title="">2017</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ilwxppn4d5hizekyd3ndvy2mii" style="color: black;">2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</a> </i> &nbsp;

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Our model is based on a convolutional neural network structure. With a specially designed training strategy, the model learns to ignore objects and focus on context only.  ...  It is fully convolutional thus highly efficient.  ...  Figure 9 : 9 Retrieved out of context faces by a SFC network. Table 2 : 2 Neural network structure summary for the base network.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/cvpr.2017.136">doi:10.1109/cvpr.2017.136</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/cvpr/SunJ17.html">dblp:conf/cvpr/SunJ17</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lfaic5wrp5gard7qxtchcd3ohi">fatcat:lfaic5wrp5gard7qxtchcd3ohi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20171209090016/http://openaccess.thecvf.com:80/content_cvpr_2017/papers/Sun_Seeing_What_Is_CVPR_2017_paper.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c7/65/c765d1a9e00cefc8dd25bf4cd9075469f21aa2d7.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/cvpr.2017.136"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>
Citation

Jin Sun, David W. Jacobs. "Seeing What is Not There: Learning Context to Determine Where Objects are Missing." 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017) 1234-1242

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