Article
Chemistry, Analytical
Dingzhong Feng, Shanyu He, Zihao Zhou, Ye Zhang
Summary: This paper proposes a novel feature extraction method called principal component local preservation projections (PCLPP) for finger vein recognition. The method combines principal component analysis (PCA) and locality preserving projections (LPP) to construct a projection matrix that preserves both global and local features of the image, thereby improving the accuracy of image recognition.
Article
Computer Science, Artificial Intelligence
Mingjie He, Jie Zhang, Shiguang Shan, Xiao Liu, Zhongqin Wu, Xilin Chen
Summary: In this paper, a novel method for simulating occlusion by dropping the activations of a group of neurons is proposed, along with an attention module to improve the contributions of non-occluded regions. Experimental results show that the proposed method achieves significant improvements in the robustness and accuracy of face recognition.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2022)
Article
Computer Science, Artificial Intelligence
Xiaohuan Lu, Jiang Long, Jie Wen, Lunke Fei, Bob Zhang, Yong Xu
Summary: In this paper, a novel method called LPP_SGE is proposed for unsupervised dimensionality reduction. LPP_SGE introduces a novel adaptive graph learning model and obtains the intrinsic graph and projection in a unified framework by fully exploring the representation information and distance information of the original data. It simultaneously captures the representation information and distance information in one term. Moreover, LPP_SGE enhances the robustness by introducing an 'l2,1' norm based projection constraint to select the most discriminative features from the complex data.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Yifan Xia, Jiayi Ma
Summary: This paper proposes a novel framework called LOcality-guided Global-preserving Optimization (LOGO) for feature matching. The framework utilizes a graph-based optimization approach to identify inliers and remove mismatches, enhancing robustness to outliers. It also introduces a locality-guided matching strategy and local affine transformations for various scenarios.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2022)
Article
Computer Science, Information Systems
Leila Boussaad, Aldjia Boucetta
Summary: This paper examines the effectiveness of deep-learning based methods for age-invariant face recognition. Five popular pre-trained deep-convolutional neural network models are evaluated on a widely used face-aging database, and the AlexNet model is found to be the most promising for feature extraction. The experimental results demonstrate the potential of convolutional neural networks in face recognition across age progression.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Jiayi Ma, Kaining Zhang, Junjun Jiang
Summary: A novel appearance-based loop closure detection system is proposed in this work, which selects candidate frames using Super-features and ASMK, and verifies loops using LPM-GC algorithm. Experimental results demonstrate that the proposed method achieves good performance in loop closure detection task.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2023)
Article
Engineering, Electrical & Electronic
Ning Liu, Zhihui Lai, Xuechen Li, Yudong Chen, Dongmei Mo, Heng Kong, Linlin Shen
Summary: In this paper, a novel regression method called Locality Preserving Robust Regression (LPRR) is proposed to address the issues encountered by conventional L-2, L-1 norm regression methods. Experimental results demonstrate that LPRR outperforms some famous subspace learning methods in classification tasks.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Zhengzheng Sun, Lianfang Tian, Qiliang Du, Jameel A. Bhutto
Summary: This paper proposes a novel loss function, called Hardness Loss, which adaptively assigns weights to misclassified hard samples by considering multiple training status and feature position information. Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches in various face recognition scenarios.
APPLIED INTELLIGENCE
(2023)
Article
Automation & Control Systems
Ye Lin, Keren Fu, Shenggui Ling, Jiang Wang, Peng Cheng
Summary: This article proposes a method to enhance identity preservation and visual quality in synthesizing face sketches and photos using deep features and interpolated convolutional neural networks. Evaluation results show that this method outperforms existing methods and is qualified for practical applications.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Information Systems
Jianqi Liu, Zhiwei Zhao, Pan Li, Geyong Min, Huiyong Li
Summary: This article proposes an Enhanced Embedded AutoEncoders framework for attribute-preserving face de-identification, which can protect personal identity while retaining desired face attributes. Experimental results show that the framework outperforms existing methods in terms of data utility, with an average improvement of 3.42% to 26.22%, indicating its effectiveness in retaining face attributes and protecting personal identity.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Alaa Eleyan
Summary: This study investigates the impact of feature fusion on face recognition performance by fusing different feature descriptors. The results show that fused feature descriptors can significantly improve performance, especially when the training set is limited.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Muhammad Aminu, Noor Atinah Ahmad
Summary: By incorporating a locality preserving feature, LPPLSDA enhances the performance of partial least squares discriminant analysis, especially in face recognition tasks. Experimental results consistently show that LPPLSDA outperforms the conventional PLS-DA method on various benchmarked face databases.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
I. Michael Revina, W. R. Sam Emmanuel
Summary: Facial expression recognition is a powerful tool for social communication, involving preprocessing, feature extraction, and classification stages, with performance of different FER techniques compared based on the number of expressions recognized and algorithm complexity.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2021)
Article
Mathematical & Computational Biology
Hongming Liu, Yunyuan Gao, Jianhai Zhang, Juanjuan Zhang
Summary: Existing epileptic seizure automatic detection systems often face difficulties caused by high-dimensional electroencephalogram (EEG) features. In this paper, a method based on supervised locality preserving canonical correlation analysis (SLPCCA) is proposed to effectively use both the sample category information and nonlinear relationships between features. The experimental results show that the proposed method achieves excellent classification accuracy compared with several state-of-the-art methods.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2022)
Article
Computer Science, Information Systems
Ratanak Khoeun, Watcharaphong Yookwan, Ponlawat Chophuk, Annupan Rodtook, Krisana Chinnasarn
Summary: The effectiveness of existing facial expression recognition approaches is hindered by the use of facial masks during the Covid-19 outbreak. This study proposes a new method called Star-Like Particle Polygon Estimation (SLPPE) for extracting features from partially obscured facial images. By using SLPPE to extract probability-based feature vectors, the proposed method achieves higher accuracy (99.01%, 98.7%, and 94.62%) compared to common CNN approaches on CK+, FER2013, and RAF-DB datasets.
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)