Article
Computer Science, Information Systems
Marcel Grimmer, Raghavendra Ramachandra, Christoph Busch
Summary: Face Age Progression (FAP) involves synthesizing face images to simulate aging effects and predict future appearances, with recent advancements in deep generative networks improving visual fidelity and aging accuracy. However, a systematic comparison of different methods is needed to accelerate research and address open challenges in the field.
Article
Computer Science, Artificial Intelligence
Hongyu Yang, Di Huang, Yunhong Wang, Anil K. Jain
Summary: The study introduces a novel generative adversarial network approach to achieve facial age progression while ensuring aging accuracy and maintaining individual identity stability. The method employs an adversarial learning scheme to train a single generator and multiple parallel discriminators simultaneously, resulting in smooth continuous face aging sequences.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
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, Information Systems
Neha Sharma, Reecha Sharma, Neeru Jindal
Summary: Research on face age progression aims to accurately generate aged faces using AttentionGAN and SRGAN, addressing the challenge of unnatural modifications in existing approaches. AttentionGAN utilizes attention and content masks to produce desired results, while SRGAN generates high-resolution aged images for detailed information in images.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Muhammad Hamza, Samabia Tehsin, Hanen Karamti, Norah Saleh Alghamdi
Summary: This research focuses on the detection of morphing attacks in facial recognition systems. A robust detection mechanism based on deep learning is proposed, along with image enhancement and feature combination techniques. The results show promising performance in detecting morphing attacks.
Article
Computer Science, Information Systems
Luis Carabe, Eduardo Cermeno
Summary: Face identification technology is increasingly used for access control applications, but faces challenges from classification algorithms and impersonation attacks. Morphing is a common method for such attacks, allowing modification of features to impersonate someone else.
Article
Computer Science, Artificial Intelligence
Cheng Yu, Wenmin Wang, Honglei Li, Roberto Bugiolacchi
Summary: This study proposes a novel method based on StyleGAN for morphing real faces by adding regularization to style optimization. By labeling synthesized faces and utilizing logistic regression, interpretable directions in latent space are discovered for handling real face morphing under different layer representations, resulting in more diverse and realistic outputs.
Article
Computer Science, Artificial Intelligence
Tsung-Ren Huang, Shin-Min Hsu, Li-Chen Fu
Summary: This study proposes using face morphing as a novel way of data augmentation to synthesize faces that express different degrees of a designated emotion, in order to improve the recognition of emotional intensity in facial emotional recognition systems. The approach has been successfully validated on both humans and machines.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2023)
Article
Computer Science, Theory & Methods
Le Qin, Fei Peng, Min Long
Summary: This paper introduces a new method for detecting and locating face morphing attacks. It addresses the weaknesses of traditional methods by using different loss functions. Experimental results demonstrate its capability in accurate localization and strong generalizability, as well as enhanced robustness to low-resolution and non-frontal images.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2022)
Article
Computer Science, Software Engineering
Ali Elmahmudi, Hassan Ugail
Summary: The paper introduces a new method for generating realistic aged faces using template faces and a pre-trained convolutional neural network based on the VGG-face model for validation. Experimental results demonstrate that this approach achieves accuracy, efficiency, and flexibility in facial age progression or regression.
Article
Computer Science, Information Systems
Ramachandra Raghavendra, Guoqiang Li
Summary: This study proposes a novel framework for detecting face morphing attacks based on a single image. By processing multimodal regions and extracting different features, such as eyes, nose, and mouth, the proposed method outperforms existing methods in terms of performance.
Article
Environmental Sciences
Jingzhong Li, Kainan Mao
Summary: This method proposes a Dynamic Time Warping (DTW) distance-based morphing method for continuous generalization of linear features. It considers both local and global characteristics by minimizing the total cost between vertices and generates continuous and smooth geometric shapes.
GEOCARTO INTERNATIONAL
(2023)
Article
Engineering, Electrical & Electronic
Jiawei Chen, Junlin Hu
Summary: Metric learning aims to learn a mapping relationship to reduce intraclass distance and increase interclass distance. The proposed weakly supervised compositional metric learning method effectively optimizes weight combination and verification accuracy.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Computer Science, Information Systems
S. Akhlaghi, H. Hassanpour
Summary: Head pose variations pose a challenge in face recognition, and frontalization methods can help address this issue. This research introduces a weighted averaging algorithm for frontalization, utilizing matrix rank minimization and classical image domain transform, showing superiority compared to existing methods.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
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
Xiangbo Shu, Jinhui Tang, Guo-Jun Qi, Wei Liu, Jian Yang
Summary: This work proposes a novel Hierarchical Long Short-Term Concurrent Memory (H-LSTCM) model for recognizing human interactions in videos by combining individual dynamics and group dynamics to capture the long-term inter-related dynamics of human interactions. Experimental results validate the effectiveness of the proposed model.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Article
Computer Science, Information Systems
Ruipeng Zhang, Xiangbo Shu, Rui Yan, Jiachao Zhang, Yan Song
Summary: The paper proposes a SAED framework, utilizing an encoder-decoder model to simulate the dependencies in human motion, with ConvGRU and SAM mechanism to capture motion information. Experimental results show the effectiveness of this method compared to others.
MULTIMEDIA SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Xiangbo Shu, Jiawen Yang, Rui Yan, Yan Song
Summary: This work focuses on the challenging task of elderly activity recognition and proposes a novel Expansion-Squeeze-Excitation Fusion Network (ESE-FN) to effectively address the problem. By attentively fusing multi-modal features from RGB videos and skeleton sequences, the ESE-FN achieves the best accuracy compared with state-of-the-art methods on a largest-scale elderly activity dataset. The proposed ESE-FN is also comparable to other methods in terms of normal action recognition task.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Jinhui Tang, Xiangbo Shu, Rui Yan, Liyan Zhang
Summary: This work addresses the problem of group activity recognition by exploring human motion characteristics. Traditional methods overlook certain relevant motions while overstating irrelevant ones. To overcome this, the authors propose the Spatio-Temporal Context Coherence (STCC) and Global Context Coherence (GCC) constraints to capture relevant motions and quantify their contributions to the group activity. They introduce a novel Coherence Constrained Graph LSTM (CCG-LSTM) with STCC and GCC to effectively recognize group activity by modeling relevant motions and suppressing irrelevant ones.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Information Systems
Xiangyu Zhao, Peng Huang, Xiangbo Shu
Summary: This paper investigates the issues in feature learning methods based on CNN and proposes a new module based on wavelet attention for image classification. Experimental results demonstrate significant improvements in accuracy using this approach.
MULTIMEDIA SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xiangbo Shu, Liyan Zhang, Guo-Jun Qi, Wei Liu, Jinhui Tang
Summary: This paper proposes a novel Skeleton-Joint Co-Attention Recurrent Neural Networks (SC-RNN) framework that can capture both the spatial coherence among joints and the temporal evolution among skeletons for human motion prediction. By constructing a joint feature map, designing a Skeleton-Joint Co-Attention mechanism, and embedding an SCA-enhanced GRU variant, the experimental results demonstrate the superiority of the proposed method over competing methods.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Software Engineering
Jiachao Zhang, Yang Gao, Yi Xu, Yunbin Huang, Yanming Yu, Xiangbo Shu
Summary: This paper proposes an image stitching technique using computational blending zone, which utilizes dynamic programming to select optimal seam-lines and optimal regions for image blending, in order to eliminate stitching traces and ghosting.
Article
Computer Science, Artificial Intelligence
Rui Yan, Xiangbo Shu, Chengcheng Yuan, Qi Tian, Jinhui Tang
Summary: The study proposes a novel Position-aware Participation-Contributed Temporal Dynamic Model, which focuses on capturing different types of key actors and their behaviors in group activities. By incorporating position-aware interaction modules and aggregation long short-term memory, the model aims to improve the recognition of key actors' contributions to the group activities in video clips.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Rui Yan, Lingxi Xie, Jinhui Tang, Xiangbo Shu, Qi Tian
Summary: This paper proposes a Hierarchical Graph-based Cross Inference Network (HiGCIN) for group activity recognition. It constructs, learns, and infers three levels of information, namely body-region level, person level, and group-activity level. The approach is effective in capturing spatiotemporal dependencies and inferring with multilevel visual cues.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Xiangbo Shu, Binqian Xu, Liyan Zhang, Jinhui Tang
Summary: In the semi-supervised skeleton-based action recognition task, the current mainstream approach of contrastive learning has limitations in learning global-granularity features, dealing with ambiguous pairs, and contrasting cross-granularity pairs. To address these limitations, the proposed MAC-Learning method conducts inter- and intra-granularity contrastive pretext tasks to learn multi-granularity representations and utilizes the MAC-Loss to measure the agreement/disagreement between soft-positive/negative pairs. Experimental results on NTU RGB+D and Northwestern-UCLA datasets demonstrate the superiority of MAC-Learning in semi-supervised skeleton-based action recognition tasks.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Rui Yan, Lingxi Xie, Xiangbo Shu, Liyan Zhang, Jinhui Tang
Summary: Previous methods encode multiple pieces of information independently and concatenate them for classification, ignoring the potential role of instance information in the process of visual perception. This study presents a framework to progressively extract, reason, and predict dynamic cues of moving instances from videos for compositional action recognition.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Binqian Xu, Xiangbo Shu, Jiachao Zhang, Guangzhao Dai, Yan Song
Summary: A novel spatiotemporal decouple-and-squeeze contrastive learning (SDS-CL) framework is proposed to comprehensively learn more abundant representations of skeleton-based actions. By jointly contrasting spatial-squeezing features, temporal-squeezing features, and global features, SDS-CL achieves performance gains compared with other competitive methods, as demonstrated by extensive experimental results on four public datasets.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Binqian Xu, Xiangbo Shu, Yan Song
Summary: In this research, a novel X-invariant Contrastive Augmentation and Representation learning (X-CAR) framework is proposed to obtain X-invariant features by adaptively augmenting and representing skeleton sequences. The method achieves better accuracy compared with other competitive methods in the semi-supervised action recognition scenario.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2022)
Article
Computer Science, Artificial Intelligence
Longquan Dai, Jinhui Tang
Summary: iFlowGAN learns an invertible flow through adversarial learning for image-to-image translation and addresses the redundancy issue between forward and backward mappings in existing generative models.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
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)
