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Multicolumn Networks for Face Recognition [article]

Weidi Xie, Andrew Zisserman
2018 arXiv   pre-print
Comparing with the previous state-of-the-art architectures trained with the same dataset (VGGFace2), our Multicolumn Networks show an improvement of between 2-6% on the IARPA IJB face recognition benchmarks  ...  The objective of this work is set-based face recognition, i.e. to decide if two sets of images of a face are of the same person or not.  ...  Government is authorized to reproduce and distribute reprints for Governmental purpose notwithstanding any copyright annotation thereon.  ... 
arXiv:1807.09192v1 fatcat:37wudxugf5cdblnutb5uj2ox5m

Group Behavior Pattern Recognition Algorithm Based on Spatio-Temporal Graph Convolutional Networks

Xinfang Chen, Venkata Dinavahi, Shah Nazir
2021 Scientific Programming  
Therefore, a group behavior pattern recognition algorithm based on spatio-temporal graph convolutional network is proposed in this paper, aiming at group density analysis and group behavior recognition  ...  With the rapid growth of population, more diverse crowd activities, and the rapid development of socialization process, group scenes are becoming more common, so the demand for modeling, analyzing, and  ...  Acknowledgments is work was supported by Special Funds for Basic Scientific Research in Central Universities (ZY20215126) and China Scholarship Fund.  ... 
doi:10.1155/2021/2934943 fatcat:zqn7agiasrhu3lhg5nki3zqrhy

Single-Image Crowd Counting using Multi-Column Neural Network

Rinku Mahesh Sharma
2020 International Journal of Computer Applications  
The technique used for the crowd detection and crowd density estimation is through the Multicolumn Convolution Neural Network architecture.  ...  One of the appropriate method that can accurately estimate the crowd count from an image with arbitrary crowd density and arbitrary perspective is using the state-of-the-art i.e. convolution neural network  ...  The future work of the proposed system can be extended to, face recognition in crowd for visual surveillance system which can handle challenges such as blurred and overlapped faces in crowded areas.  ... 
doi:10.5120/ijca2020920598 fatcat:uepvnxrplvgbhnfy4t6yca7vea

Scene perception system for visually impaired based on object detection and classification using multimodal deep convolutional neural network

Baljit Kaur, Jhilik Bhattacharya
2019 Journal of Electronic Imaging (JEI)  
The object detection and classification framework exploits a multi-modal fusion based faster RCNN using motion, sharpening and blurring filters for efficient feature representation.  ...  Features Features with deep learning for object detection [27] , ing box recognition [29] . It is noticed that these object detection networks fine tuned VGG16 with PASCAL dataset.  ...  Social interaction assistance for individual with visual disability are also provided to some extent in the form of person recognition, facial expression recognition [20] . proposed visual and range information  ... 
doi:10.1117/1.jei.28.1.013031 fatcat:tqrienpl5ff7ddymmbsilmruhy

Discriminability Distillation in Group Representation Learning [article]

Manyuan Zhang, Guanglu Song, Hang Zhou, Yu Liu
2020 arXiv   pre-print
Comprehensive experiments on various tasks have proven the effectiveness of DDL for both accuracy and efficiency.  ...  The proposed DDL can be flexibly plugged into many group-based recognition tasks without influencing the original training procedures.  ...  Suppose a base network M is trained for the element-based recognition task.  ... 
arXiv:2008.10850v2 fatcat:q5uht37akrgx7eit2wmhaw2vhm

Automatic Handwritten Indian Scripts Identification

Rajmohan Pardeshi, B.B. Chaudhuri, Mallikarjun Hangarge, K.C. Santosh
2014 2014 14th International Conference on Frontiers in Handwriting Recognition  
This method will be worked efficiently compared to the conventional character recognition soft wares.  ...  The increase in usage of handheld devices which accept handwritten input has created a growing demand for algorithms that can efficiently analyze and retrieve handwritten data.  ...  in face of varied size, for banks or zip codes on envelopes for postal services shapes and fonts.  ... 
doi:10.1109/icfhr.2014.69 dblp:conf/icfhr/PardeshiCHS14 fatcat:rkz35bvhkrbbncbmn3twk5xoey

Crowd Counting Based on Multiresolution Density Map and Parallel Dilated Convolution

Jingfan Tang, Meijia Zhou, Pengfei Li, Min Zhang, Ming Jiang, Ferruccio Damiani
2021 Scientific Programming  
with fewer parameters while reducing the loss of multiscale information; (2) the multiresolution density map module (MDM) that contains three-branch networks for extracting spatial contact information  ...  The current crowd counting tasks rely on a fully convolutional network to generate a density map that can achieve good performance.  ...  [18] designed a selector to input image blocks to specific branches for feature extraction, further reducing the computational complexity of multicolumn networks. rough experiments, Li et al.  ... 
doi:10.1155/2021/8831458 fatcat:mk2cs4ul3vdz5h7dyqybqdllgi

LARNet: Lie Algebra Residual Network for Face Recognition [article]

Xiaolong Yang, Xiaohong Jia, Dihong Gong, Dong-Ming Yan, Zhifeng Li, Wei Liu
2021 arXiv   pre-print
Based on this theoretical finding, we further design a Lie Algebraic Residual Network (LARNet) for tackling pose robust face recognition.  ...  Face recognition is an important yet challenging problem in computer vision.  ...  Multicolumn Network (Xie & Zisserma, 2018) and Neural Aggregation Network (NAN) (Yang et al., 2017) propose to use more information, such as a set of images or videos as input, to tackle the potential  ... 
arXiv:2103.08147v2 fatcat:74n3vqiwubdlldcrlnycfdwbie

CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition [article]

Yuge Huang, Yuhan Wang, Ying Tai, Xiaoming Liu, Pengcheng Shen, Shaoxin Li, Jilin Li, Feiyue Huang
2020 arXiv   pre-print
As an emerging topic in face recognition, designing margin-based loss functions can increase the feature margin between different classes for enhanced discriminability.  ...  In this work, we propose a novel Adaptive Curriculum Learning loss (CurricularFace) that embeds the idea of curriculum learning into the loss function to achieve a novel training strategy for deep face  ...  Introduction The success of Convolutional Neural Networks (CNNs) on face recognition can be mainly credited to: enormous training data, network architectures, and loss functions.  ... 
arXiv:2004.00288v1 fatcat:lfgidaiktrgqhnh7i5qcnt2a74

PoseFace: Pose-Invariant Features and Pose-Adaptive Loss for Face Recognition [article]

Qiang Meng, Xiaqing Xu, Xiaobo Wang, Yang Qian, Yunxiao Qin, Zezheng Wang, Chenxu Zhao, Feng Zhou, Zhen Lei
2021 arXiv   pre-print
Despite the great success achieved by deep learning methods in face recognition, severe performance drops are observed for large pose variations in unconstrained environments (e.g., in cases of surveillance  ...  To address it, current methods either deploy pose-specific models or frontalize faces by additional modules.  ...  faces are intrinsically hard for recognition.  ... 
arXiv:2107.11721v1 fatcat:2hdiixh5bvfjbdpvlqb5b25pzy

The Study of The Convolutional Neural Networks Applications

Ahmed Shamsaldin, Polla Fattah, Tarik Rashid, Nawzad Al-Salihi
2019 UKH Journal of Science and Engineering  
In this work, a brief description of the applications of CNNs in two areas will be presented: First, in computer vision, generally, that is, scene labeling, face recognition, action recognition, and image  ...  A convolutional neural networks (CNN) is becoming the star of deep learning as it gives the best and most precise results when cracking real-world problems.  ...  Face Recognition: a sequence of correlated problems arises with face recognition, these are as follows: 1. Recognizing the faces in the picture. 2.  ... 
doi:10.25079/ukhjse.v3n2y2019.pp31-40 fatcat:bpjbowttzjcbncisb5z7kovw2m

Jointly De-biasing Face Recognition and Demographic Attribute Estimation [article]

Sixue Gong, Xiaoming Liu, Anil K. Jain
2020 arXiv   pre-print
We present a novel de-biasing adversarial network (DebFace) that learns to extract disentangled feature representations for both unbiased face recognition and demographics estimation.  ...  The proposed network consists of one identity classifier and three demographic classifiers (for gender, age, and race) that are trained to distinguish identity and demographic attributes, respectively.  ...  representation for face recognition.  ... 
arXiv:1911.08080v4 fatcat:2cavhrnfezggjh6jffhpxzhfoy

Inducing Predictive Uncertainty Estimation for Face Recognition [article]

Weidi Xie, Jeffrey Byrne, Andrew Zisserman
2020 arXiv   pre-print
Knowing when an output can be trusted is critical for reliably using face recognition systems.  ...  To this end, we propose a method for generating image quality training data automatically from 'mated-pairs' of face images, and use the generated data to train a lightweight Predictive Confidence Network  ...  Funding for this research is also provided by the EPSRC Programme Grant Seebibyte EP/M013774/1.  ... 
arXiv:2009.00603v1 fatcat:frhrtrawtbcmfjritvlbg7kz74

Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, and Performance Evaluation

Naveed Ilyas, Ahsan Shahzad, Kiseon Kim
2019 Sensors  
Despite many challenges, such as occlusion, clutter, and irregular object distribution and nonuniform object scale, convolutional neural networks are a promising technology for intelligent image crowd  ...  Finally, we conclude this article by presenting our key observations, providing strong foundation for future research directions while designing convolutional-neural-network-based crowd-counting techniques  ...  Traditional handcrafted methods do not perform well in harsh and densely crowded events, and could be replaced by CNN-based face recognition and detection techniques for better crowd analysis [132] [133  ... 
doi:10.3390/s20010043 pmid:31861734 pmcid:PMC6983207 fatcat:gvso42grpjbw5ptdb23sdfeuwu

Fast and Reliable Probabilistic Face Embeddings in the Wild [article]

Kai Chen, Qi Lv, Taihe Yi
2021 arXiv   pre-print
recognition.  ...  Probabilistic Face Embeddings (PFE) can improve face recognition performance in unconstrained scenarios by integrating data uncertainty into the feature representation.  ...  Grad-CAM visualization of IJB-B dataset face embeddings method for cross-modal face recognition and retrieval, face adversarial attacks, face interpretability, etc. APPENDIX A.  ... 
arXiv:2102.04075v3 fatcat:urin4gvonvasrbh5qpig56jnmm
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