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Showing 1-25 of 9,917 resultsfor "Index Terms":Face Dataset
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A Multi-Face Challenging Dataset for Robust Face Recognition
2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)
Year: 2018 | Conference Paper |
Cited by: Papers (6)
Face recognition in images is an active area of interest among the computer vision researchers. However, recognizing human face in an unconstrained environment, is a relatively less-explored area of research. Multiple face recognition in unconstrained environment is a challenging task, due to the variation of view-point, scale, pose, illumination and expression of the face images. Partial occlusio...Show More
Chinese Face Dataset for Face Recognition in an Uncontrolled Classroom Environment
Nianfeng Li;Xiangfeng Shen;Liyan Sun;Zhiguo Xiao;Tianjiao Ding;Tiansheng Li;Xinhang Li
Since the position of the classroom surveillance camera is not fixed, the angle of the face captured through the surveillance video is also different. The deep learning-based face verification model has achieved good results in controlled environments, but there is still much room for improvement in the face verification ability in uncontrolled environments. The performance of the model depends no...Show More
Dataset Cleaning — A Cross Validation Methodology for Large Facial Datasets using Face Recognition
2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX)
Year: 2020 | Conference Paper |
Cited by: Papers (4)
In recent years, large “in the wild” face datasets have been released in an attempt to facilitate progress in tasks such as face detection, face recognition, and other tasks. Most of these datasets are acquired from webpages with automatic procedures. As a consequence, noisy data are often found. Furthermore, in these large face datasets, the annotation of identities is important as they are used ...Show More
Deep transfer network for face recognition using 3D synthesized face
2017 IEEE Visual Communications and Image Processing (VCIP)
Year: 2017 | Conference Paper |
Cited by: Papers (1)
Face recognition has experienced a flurry of advances with deep learning. However, training a model requires a lot of data. In order to meet this condition, some researchers use the 3D rendering technique to synthesize fake face images to expand the training data. Experimental results have demonstrated that this method is an effective way. There exist, however, dataset bias between the real 2D rea...Show More
Masked Face Recognition: Progress, Dataset, and Dataset Generation
2021 3rd International Conference on Cybernetics and Intelligent System (ICORIS)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
Face recognition is one of the oldest and famous research in computer vision and has improved over the years. However, most research of this topic are designed to recognize full human face. Ever since COVID-19 pandemic, most people must wear face mask when going outside. This create new challenges for existing face recognition as it is proven to be ineffective recognizing people wearing mask. This...Show More
Celebrity Face Matching Using Deep Learning
2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)
Year: 2022 | Conference Paper |
Face detection and face recognition are hot topics in computer vision and have many real-life applications. In this paper we present a celebrity face matching system to match a random human face with celebrities' faces taken from the Pins Face Recognition dataset. Several deep learning models for face detection and face recognition are explored and compared. An initial accuracy of over 89.4% is ac...Show More
Celeb-500K: A Large Training Dataset for Face Recognition
2018 25th IEEE International Conference on Image Processing (ICIP)
Year: 2018 | Conference Paper |
Cited by: Papers (23)
In this paper, we propose a large training dataset named Celeb-500K for face recognition, which contains 50M images from 500K persons. To better facilitate academic research, we clean Celeb-500K to obtain Celeb-500K-2R, which contains 25M aligned face images from 365K persons. Based on the developed dataset, we achieve state-of-the-art face recognition performance and reveal two important observat...Show More
In-House Facial Data Collection for Face Recognition Accuracy: Dataset Accuracy Analysis
Taufiq Maulana Firdaus;Tien Fabrianti Kusumasari;Sinung Suakanto;Oktariani Nurul Pratiwi
2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
The face recognition system is a field of research that can help identify a person for either a security system or a search system because faces are distinctive from each person. The development of a human face recognition model requires several main components, such as scale, light, and expression. This study aims to collect accurate samples of Indonesian faces for face detection algorithms. Dete...Show More
In-House Facial Data Collection for Face Recognition Accuracy: Dataset Accuracy Analysis
Taufiq Maulana Firdaus;Tien Fabrianti Kusumasari;Sinung Suakanto;Oktariani Nurul Pratiwi
2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)
Year: 2023 | Conference Paper |
Masked Face Recognition Dataset and Application
Zhongyuan Wang;Baojin Huang;Guangcheng Wang;Peng Yi;Kui Jiang
During COVID-19 coronavirus epidemic, almost everyone wears a mask to prevent the spread of virus. It raises a problem that the traditional face recognition model basically fails in the scene of face-based identity verification, such as security check, community visit check-in, etc. Therefore, it is imminent to boost the performance of masked face recognition. Most recent advanced face recognition...Show More
A Pipeline to Improve Face Recognition Datasets and Applications
2018 International Conference on Image and Vision Computing New Zealand (IVCNZ)
Year: 2018 | Conference Paper |
Cited by: Papers (2)
Face recognition has a wide practical applicability in various contexts, for example, detecting students attending a lecture at university, identifying members in a gym or monitoring people in an airport. Recent methods based on Convolutional Neural Network (CNN), such as FaceNet, achieved state-of-the-art performance in face recognition. Inspired from this work, we propose a pipeline to improve f...Show More
Generating Masked Facial Datasets Using Dlib-Machine Learning Library
2022 4th International Conference on Advanced Science and Engineering (ICOASE)
Year: 2022 | Conference Paper |
In 2020, the COVID-19 pandemic spread globally, leading to countries imposing health restrictions on people, including wearing masks, to prevent the spread of the disease. Wearing a mask significantly decreases distinguishing ability due to its concealment of the main facial features. After the outbreak of the pandemic, the existing datasets became unsuitable because they did not contain images of...Show More
Masked Face Recognition using Deep Learning based on Unmasked Area
2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
Researches related to face recognition find new problems, especially due to current lifestyle change by wearing a face mask. A masked face means that features or information contained in the face are missing. Hence, some modifications should be made to the existing face recognition system in this new situation. On the other hand, some studies indicate that face recognition using partial faces stil...Show More
Low-Resolution Face Recognition in the Wild with Mixed-Domain Distillation
2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM)
Year: 2019 | Conference Paper |
Cited by: Papers (3)
Low-resolution face recognition in the wild still is an open problem. In this paper, we propose to address this problem via a novel learning approach called Mixed-Domain Distillation (MDD). The approach applies a teacher-student framework to mix and distill knowledge from four different domain datasets, including private high-resolution, public high-resolution, public low-resolution web and target...Show More
Longitudinal Study of Child Face Recognition
2018 International Conference on Biometrics (ICB)
Year: 2018 | Conference Paper |
Cited by: Papers (41)
We present a longitudinal study of face recognition performance on Children Longitudinal Face (CLF) dataset containing 3,682 face images of 919 subjects, in the age group [2,18] years. Each subject has at least four face images acquired over a time span of up to six years. Face comparison scores are obtained from (i) a state-of-the-art COTS matcher (COTS-A), (ii) an open-source matcher (FaceNet), ...Show More
A Tilt-Angle Face Dataset And Its Validation
Nanxi Wang;Zhongyuan Wang;Zheng He;Baojin Huang;Liguo Zhou;Zhen Han
2021 IEEE International Conference on Image Processing (ICIP)
Year: 2021 | Conference Paper |
Cited by: Papers (6)
Since the surveillance cameras are usually mounted at a high position to overlook targets, tilt-angle faces on overhead view are common in the public video surveillance environment. Face recognition approaches based on deep learning models have achieved excellent performance, but there remains a large gap for the overlooking surveillance scenarios. The results of face recognition depend not only o...Show More
LFR face dataset:Left-Front-Right dataset for pose-invariant face recognition in the wild
2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT)
Year: 2020 | Conference Paper |
Cited by: Papers (14)
In this work, a new multitask convolutional neural network (CNN) is proposed aiming for the recognition of face under pose variations. Furthermore, the combination of pose estimation for each corresponding pose in a separate fashion allows robust face recognition in presence of various facial expressions as well as low illuminations. First, a CNN model for pose estimation is proposed. The pose est...Show More
An Automatic System for Unconstrained Video-Based Face Recognition
Jingxiao Zheng;Rajeev Ranjan;Ching-Hui Chen;Jun-Cheng Chen;Carlos D. Castillo;Rama Chellappa
Although deep learning approaches have achieved performance surpassing humans for still image-based face recognition, unconstrained video-based face recognition is still a challenging task due to large volume of data to be processed and intra/inter-video variations on pose, illumination, occlusion, scene, blur, video quality, etc. In this work, we consider challenging scenarios for unconstrained v...Show More
When Face Recognition Meets Occlusion: A New Benchmark
Baojin Huang;Zhongyuan Wang;Guangcheng Wang;Kui Jiang;Kangli Zeng;Zhen Han;Xin Tian;Yuhong Yang
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2021 | Conference Paper |
Cited by: Papers (30)
The existing face recognition datasets usually lack occlusion samples, which hinders the development of face recognition. Especially during the COVID-19 coronavirus epidemic, wearing a mask has become an effective means of preventing the virus spread. Traditional CNN-based face recognition models trained on existing datasets are almost ineffective for heavy occlusion. To this end, we pioneer a sim...Show More
Common Feature Discriminant Analysis for Matching Infrared Face Images to Optical Face Images
In biometrics research and industry, it is critical yet a challenge to match infrared face images to optical face images. The major difficulty lies in the fact that a great discrepancy exists between the infrared face image and corresponding optical face image because they are captured by different devices (optical imaging device and infrared imaging device). This paper presents a new approach cal...Show More
Multi-angled Face Segmentation and Identification Using Limited Data
2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)
Year: 2019 | Conference Paper |
This paper introduces a different approach to face segmentation that aims to improve face recognition when given large pose angles and limited training data. Face segmentation is achieved by extracting landmarks which are manipulated in such a way as to normalize unseen data with a classification model. The approach is compared with related systems, followed by further tests that show consistent r...Show More
On Recognizing Occluded Faces in the Wild
2021 International Conference of the Biometrics Special Interest Group (BIOSIG)
Year: 2021 | Conference Paper |
Cited by: Papers (14)
Facial appearance variations due to occlusion has been one of the main challenges for face recognition systems. To facilitate further research in this area, it is necessary and important to have occluded face datasets collected from real-world, as synthetically generated occluded faces cannot represent the nature of the problem. In this paper, we present the Real World Occluded Faces (ROF) dataset...Show More
Heterogeneous face recognition (HFR), which refers to matching face images with different modalities, is essential to public safety. Although HFR has made promising progress in recent years, disguised faces in HFR scenarios still remain a major challenge for the following reasons. First, most existing HFR methods focus on traditional scenarios without disguised accessories, and the performance deg...Show More
Robust Masked Face Recognition via Balanced Feature Matching
2022 IEEE International Conference on Consumer Electronics (ICCE)
Year: 2022 | Conference Paper |
Cited by: Papers (4)
Wearing a facial mask has become a must in our daily life due to the global COVID-19 pandemic. However, the performance of conventional face recognition systems severely degrades for faces occluded by masks. How to combat the effect of occlusion on face recognition is an important issue. However, the performance of existing methods developed for masked face recognition unpleasantly degrades when d...Show More
EDFace-Celeb-1 M: Benchmarking Face Hallucination With a Million-Scale Dataset
Kaihao Zhang;Dongxu Li;Wenhan Luo;Jingyu Liu;Jiankang Deng;Wei Liu;Stefanos Zafeiriou
Recent deep face hallucination methods show stunning performance in super-resolving severely degraded facial images, even surpassing human ability. However, these algorithms are mainly evaluated on non-public synthetic datasets. It is thus unclear how these algorithms perform on public face hallucination datasets. Meanwhile, most of the existing datasets do not well consider the distribution of ra...Show More
Face Recognition and Retrieval Using Cross-Age Reference Coding With Cross-Age Celebrity Dataset
IEEE Transactions on Multimedia
Cited by: Papers (227)
This paper introduces a method for face recognition across age and also a dataset containing variations of age in the wild. We use a data-driven method to address the cross-age face recognition problem, called cross-age reference coding (CARC). By leveraging a large-scale image dataset freely available on the Internet as a reference set, CARC can encode the low-level feature of a face image with a...Show More
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