Article
Computer Science, Artificial Intelligence
Jian Zhao, Shuicheng Yan, Jiashi Feng
Summary: Despite advances in face recognition, recognizing faces across ages remains a challenge. A deep Age-Invariant Model (AIM) is proposed to jointly perform face synthesis and recognition, achieving remarkable rejuvenation/aging and disentangling age variation. The model outperforms existing techniques on benchmark datasets and demonstrates promising generalization ability in unconstrained face recognition.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Yanfei Liu, Junhua Chen
Summary: This study introduces a novel Pose Conditional CycleGAN for generating frontal face images to achieve pose-invariant face recognition. Through constraints and controls on losses, the model can be trained without paired training data. Experimental results demonstrate the model's ability to synthesize high-quality frontal face images while preserving identity information.
IMAGE AND VISION COMPUTING
(2021)
Article
Engineering, Electrical & Electronic
Yangjian Huang, Haifeng Hu
Summary: Cross-age face recognition is challenging due to the significant impact of aging on facial appearance. The proposed Age Adversarial Convolutional Neural Network (AA-CNN) effectively separates aging variations from facial features for stable and person-specific recognition. Extensive experiments demonstrate the superiority and effectiveness of the AA-CNN model on various aging face datasets.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2021)
Article
Computer Science, Theory & Methods
Yingfan Tao, Wenxian Zheng, Wenming Yang, Guijin Wang, Qingmin Liao
Summary: A novel Frontal-Centers Guided Loss (FCGFace) is proposed for face recognition, which achieves better performance in handling profile faces. Compared to existing methods, FCGFace takes viewpoints into consideration and can adaptively adjust feature distribution to form compact identity clusters.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2022)
Article
Computer Science, Artificial Intelligence
Hyeonwoo Kim, Hyungjoon Kim, Jonghwa Shim, Eenjun Hwang
Summary: Kinship verification determines kin relationships through facial image analysis and has various real-life applications. However, limited labeled data and age differences in the images make kinship verification challenging. To address this, we propose a face age transformation model and a cross-age kinship verification model using generated images as training data. Comparative experiments with popular kinship datasets show that our proposed method achieves improved verification accuracy.
COMPUTER VISION AND IMAGE UNDERSTANDING
(2023)
Article
Computer Science, Artificial Intelligence
Qingyan Duan, Lei Zhang, Yan Zhang, Xinbo Gao
Summary: This paper proposes a GAN-based face frontalization method using a Bayesian induced perceptual self-representation discriminator (PSD) to address the issues in traditional GANs. The proposed method reduces model parameters and training difficulty, and achieves superior performance.
Article
Engineering, Electrical & Electronic
Yujie Hu, Yinhuai Wang, Jian Zhang
Summary: In this paper, a novel DEgradation-Aware Restoration network with GAN prior (DEAR-GAN) is proposed for face restoration tasks. By explicitly learning the degradation representations (DR), the network can adapt to various degradation levels and dynamically fuse informative features through a feature interpolation module, achieving superior restoration results compared to existing methods.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2023)
Article
Computer Science, Information Systems
Hady Pranoto, Yaya Heryadi, Harco Leslie Hendric Spits Warnars, Widodo Budiharto
Summary: Cross aging affects face recognition ability, and using synthetic face images can improve performance. A new optimized variant is proposed for generating synthetic face images at specific age groups, with improvements in accuracy and training time. Evaluation results demonstrate better accuracy in various tasks.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Chemistry, Analytical
Ren-Hung Hwang, Jia-You Lin, Sun-Ying Hsieh, Hsuan-Yu Lin, Chia-Liang Lin
Summary: Deep learning technology has developed rapidly and has been successfully applied in various fields, including face recognition. However, most previous studies on adversarial attacks assume the attacker knows the architecture and parameters of the attacked deep learning model, which is not representative of real-world scenarios. This study proposes a Generative Adversarial Network method for generating adversarial patches to carry out dodging and impersonation attacks on a black-box face recognition system, achieving a higher attack success rate than previous works.
Article
Computer Science, Artificial Intelligence
Ran He, Yi Li, Xiang Wu, Lingxiao Song, Zhenhua Chai, Xiaolin Wei
Summary: This study introduces a coupled adversarial learning (CAL) approach to address the challenging issue of VIS-NIR face matching, by conducting adversarial learning on both image and feature levels. Experimental results demonstrate that CAL not only synthesizes high-quality VIS or NIR images, but also achieves state-of-the-art recognition results.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Artificial Intelligence
Hongyu Yang, Kangkang Zhu, Di Huang, Hebeizi Li, Yunhong Wang, Liming Chen
Summary: This paper proposes a novel multimodal approach based on GAN that jointly models intensity enhancement and expression recognition, resulting in improved FER performance. Experimental results on multiple datasets validate the effectiveness of the method in both low expression intensity and general FER scenarios.
Article
Computer Science, Artificial Intelligence
Peng Wu, Conghui Zheng, Li Pan
Summary: Network Representation Learning (NRL) aims to embed nodes into a latent, low-dimensional vector space while preserving network properties. Many NRL methods use skip-gram model to achieve this by maximizing predictive probability among context nodes. However, these explicit network features may lead to loss of training samples and limited discriminative power. We propose a general and unified generative adversarial learning framework to address these issues and improve the performances of skip-gram based NRL methods.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Daniel Saez Trigueros, Li Meng, Margaret Hartnett
Summary: This paper investigates the feasibility of using synthetic data to augment face datasets, proposing a novel generative adversarial network (GAN) method that can separate identity-related attributes from non-identity-related attributes. Experimental results show that training with augmented synthetic images can improve recognition accuracy, especially effective for small datasets.
Article
Computer Science, Information Systems
Gabriel Hermosilla, Diego-Ignacio Henriquez Tapia, Hector Allende-Cid, Gonzalo Farias Castro, Esteban Vera
Summary: This article utilizes StyleGAN2 and generative adversarial networks (GANs) to create high-quality synthetic thermal images and build thermal face recognition models using deep learning. By training with thermal databases and pretrained deep learning models, the synthetic thermal database achieved 99.98% accuracy in classifying thermal face images.
Article
Computer Science, Information Systems
Le Minh Ngo, Sezer Karaoglu, Theo Gevers
Summary: A novel architecture is proposed in this paper for manipulating facial expressions, head poses, and lighting conditions from a single monocular image. The method outperforms state-of-the-art methods in various scenarios and does not require target specific training.
IEEE TRANSACTIONS ON MULTIMEDIA
(2022)
Article
Engineering, Electrical & Electronic
Qirun Huo, Jianwu Li, Yao Lu, Ziye Yan
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
(2016)
Article
Computer Science, Information Systems
Hong Wang, Jianwu Li, Zhengchao Dong
MULTIMEDIA TOOLS AND APPLICATIONS
(2018)
Article
Computer Science, Artificial Intelligence
Jianwu Li, Lu Su, Cheng Cheng
APPLIED SOFT COMPUTING
(2011)
Article
Computer Science, Artificial Intelligence
Jianwu Li, Yulong Song
Article
Computer Science, Artificial Intelligence
Zheng Wang, Jianwu Li, Mogendi Enoh
NEURAL COMPUTING & APPLICATIONS
(2019)
Article
Computer Science, Artificial Intelligence
Jianwu Li, Ge Song, Minhua Zhang
NEURAL COMPUTING & APPLICATIONS
(2020)
Article
Computer Science, Information Systems
Xiaohang Bian, Jianwu Li
Summary: This paper proposes a novel face aging/rejuvenation method named CACIAE, which uses a Res-Encoder, introduces rectangular kernel, and proposes consistent identity loss to generate more natural and identity-preserving face images. Experimental results demonstrate the effectiveness of the method.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Telecommunications
Qingyao Liu, Jianwu Li, Zhaoming Lu
Summary: The letter introduces a novel Spatial-Temporal Transformer (ST-Tran) for accurate cellular traffic prediction in communication networks. It utilizes temporal transformer block and spatial transformer block to learn and merge the temporal and spatial features of grid traffic flows, resulting in effective predictions. Experimental results on a large real-world dataset confirm the effectiveness of ST-Tran in traffic prediction.
IEEE COMMUNICATIONS LETTERS
(2021)
Article
Computer Science, Artificial Intelligence
Tianfei Zhou, Liulei Li, Xueyi Li, Chun-Mei Feng, Jianwu Li, Ling Shao
Summary: This work proposes a novel group-wise learning framework for weakly supervised semantic segmentation. The framework explicitly encodes semantic dependencies and discovers rich semantic context within a group of images. The proposed model achieves state-of-the-art performance in various benchmarks and demonstrates strong generalizability in weakly supervised object localization tasks.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2022)
Article
Computer Science, Artificial Intelligence
Tianfei Zhou, Jianwu Li, Shunzhou Wang, Ran Tao, Jianbing Shen
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2020)
Article
Computer Science, Artificial Intelligence
Guang-Hai Liu, Zuo-Yong Li, Jing-Yu Yang, David Zhang
Summary: This article introduces a novel image retrieval method that improves retrieval performance by using sublimated deep features. The method incorporates orientation-selective features and color perceptual features, effectively mimicking these mechanisms to provide a more discriminating representation.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Fengguang Peng, Zihan Ding, Ziming Chen, Gang Wang, Tianrui Hui, Si Liu, Hang Shi
Summary: RGB-Thermal (RGB-T) semantic segmentation is an emerging task that aims to improve the robustness of segmentation methods under extreme imaging conditions by using thermal infrared modality. The challenges of foreground-background distinguishment and complementary information mining are addressed by proposing a cross modulation process with two collaborative components. Experimental results show that the proposed method achieves state-of-the-art performances on current RGB-T segmentation benchmarks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Baihong Han, Xiaoyan Jiang, Zhijun Fang, Hamido Fujita, Yongbin Gao
Summary: This paper proposes a novel automatic prompt generation method called F-SCP, which focuses on generating accurate prompts for low-accuracy classes and similar classes. Experimental results show that our approach outperforms state-of-the-art methods on six multi-domain datasets.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Huikai Liu, Ao Zhang, Wenqian Zhu, Bin Fu, Bingjian Ding, Shengwu Xiong
Summary: Adverse weather conditions present challenges for computer vision tasks, and image de-weathering is an important component of image restoration. This paper proposes a multi-patch skip-forward structure and a Residual Deformable Convolutional module to improve feature extraction and pixel-wise reconstruction.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Oliver M. Crook, Mihai Cucuringu, Tim Hurst, Carola-Bibiane Schonlieb, Matthew Thorpe, Konstantinos C. Zygalakis
Summary: The transportation LP distance (TLP) is a generalization of the Wasserstein WP distance that can be applied directly to color or multi-channelled images, as well as multivariate time-series. TLP interprets signals as functions, while WP interprets signals as measures. Although both distances are powerful tools in modeling data with spatial or temporal perturbations, their computational cost can be prohibitively high for moderate pattern recognition tasks. The linear Wasserstein distance offers a method for projecting signals into a Euclidean space, and in this study, we propose linear versions of the TLP distance (LTLP) that show significant improvement over the linear WP distance in signal processing tasks while being several orders of magnitude faster to compute than the TLP distance.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Haitao Tian, Shiru Qu, Pierre Payeur
Summary: This paper proposes a method of target-dependent classifier, which optimizes the joint hypothesis of domain adaptation into a target-dependent hypothesis that better fits with the target domain clusters through an unsupervised fine-tuning strategy and the concept of meta-learning. Experimental results demonstrate that this method outperforms existing techniques in synthetic-to-real adaptation and cross-city adaptation benchmarks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Qingsen Yan, Axi Niu, Chaoqun Wang, Wei Dong, Marcin Wozniak, Yanning Zhang
Summary: Deep learning-based methods have achieved remarkable results in the field of super-resolution. However, the limitation of paired training image sets has led researchers to explore self-supervised learning. However, the assumption of inaccurate downscaling kernel functions often leads to degraded results. To address this issue, this paper introduces KGSR, a kernel-guided network that trains both upscaling and downscaling networks to generate high-quality high-resolution images even without knowing the actual downscaling process.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Yifan Chen, Xuelong Li
Summary: Gait recognition is a popular technology for identification due to its ability to capture gait features over long distances without cooperation. However, current methods face challenges as they use a single network to extract both temporal and spatial features. To solve this problem, we propose a two-branch network that focuses on spatial and temporal feature extraction separately. By combining these features, we can effectively learn the spatio-temporal information of gait sequences.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Wei Shi, Wentao Zhang, Wei-shi Zheng, Ruixuan Wang
Summary: This article proposes a simple yet effective visualization framework called PAMI, which does not require detailed model structure and parameters to obtain visualization results. It can be applied to various prediction tasks with different model backbones and input formats.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Xiaobo Hu, Jianbo Su, Jun Zhang
Summary: This paper reviews the latest technologies in pattern recognition, highlighting their instabilities and failures in practical applications. From a control perspective, the significance of disturbance rejection in pattern recognition is discussed, and the existing problems are summarized. Finally, potential solutions related to the application of compensation on features are discussed to emphasize future research directions.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Andres Felipe Posada-Moreno, Nikita Surya, Sebastian Trimpe
Summary: Convolutional neural networks are widely used in critical systems, and explainable artificial intelligence has proposed methods for generating high-level explanations. However, these methods lack the ability to determine the location of concepts. To address this, we propose a novel method for automatic concept extraction and localization based on pixel-wise aggregations, and validate it using synthetic datasets.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Peng Bao, Jianian Li, Rong Yan, Zhongyi Liu
Summary: In this paper, a novel Dynamic Graph Contrastive Learning framework, DyGCL, is proposed to capture the temporal consistency in dynamic graphs and achieve good performance in node representation learning.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Kristian Schultz, Saptarshi Bej, Waldemar Hahn, Markus Wolfien, Prashant Srivastava, Olaf Wolkenhauer
Summary: Research indicates that deep generative models perform poorly compared to linear interpolation-based methods for synthetic data generation on small, imbalanced tabular datasets. To address this, a new approach called ConvGeN, combining convex space learning with deep generative models, has been proposed. ConvGeN improves imbalanced classification on small datasets while remaining competitive with existing linear interpolation methods.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Khondaker Tasrif Noor, Antonio Robles-Kelly
Summary: In this paper, the authors propose H-CapsNet, a capsule network designed for hierarchical image classification. The network effectively captures hierarchical relationships using dedicated capsules for each class hierarchy. A modified hinge loss is utilized to enforce consistency among the involved hierarchies. Additionally, a strategy for dynamically adjusting training parameters is presented to achieve better balance between the class hierarchies. Experimental results demonstrate that H-CapsNet outperforms competing hierarchical classification networks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Lei Liu, Guorun Li, Yuefeng Du, Xiaoyu Li, Xiuheng Wu, Zhi Qiao, Tianyi Wang
Summary: This study proposes a new agricultural image segmentation model called CS-Net, which uses Simple-Attention Block and Simpleformer to improve accuracy and inference speed, and addresses the issue of performance collapse of Transformers in agricultural image processing.
PATTERN RECOGNITION
(2024)