Article
Computer Science, Information Systems
Guojun Lin, Qinrui Zhang, Shunyong Zhou, Xingguo Jiang, Hao Wu, Hairong You, Zuxin Li, Ping He, Heng Li
Summary: This paper explores the representation of testing face images for multi-feature face recognition, and proposes an extended joint similar and specific learning method to effectively address the drawbacks of the original method.
Article
Computer Science, Information Systems
Shicheng Yang, Ying Wen, Lianghua He, MengChu Zhou
Summary: This study proposes a novel method called sparse common feature-based representation (SCFR) to address the issue of undersampled face recognition encountered in IoT applications, providing better performance without the time-consuming training required by deep learning models.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Shicheng Yang, Ying Wen, Lianghua He, Mengchu Zhou, Abdullah Abusorrah
Summary: This work introduces a sparse individual low-rank component-based representation (SILR) method that effectively addresses the impact of undersampled training datasets and same intrasubject variations on classification performance by applying l(2)-norm constraint to intrasubject coefficients.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Nour Elhouda Chalabi, Abdelouahab Attia, Abderraouf Bouziane, Zahid Akhtar
Summary: This paper proposes a novel descriptor called M-BSIF for extracting distinctive and relevant features from face images. The proposed method combines monogenic signal representation and Binarized Statistical Image Feature (BSIF) to enhance the capability of face feature extraction. Experimental results on three public databases show that the proposed M-BSIF descriptor outperforms a framework using only single BSIF.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Yuan Sun, Zhenwen Ren, Chao Yang, Quansen Sun, Liwan Chen, Yanglong Ou
Summary: Image set classification has gained extensive attention due to its ability to overcome various variations, and the point-to-point distance-based methods have achieved promising performance. However, these methods fail to fully utilize the discrimination information between different gallery sets and assume the equal importance of all sets. Additionally, they often have high computational cost. To address these issues, we propose a novel method called SLSDL, which incorporates a self-weighted strategy and latent sparse normalization to enhance discrimination and reduce complexity. Experimental results demonstrate that SLSDL outperforms state-of-the-art competitors.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Xuqin Wei, Yun Shi, Weiyin Gong, Yanyun Guan
Summary: This paper introduces a novel image classification algorithm that uses an improved image representation method to generate virtual samples and designs a weight fusion scheme. The proposed algorithm improves the accuracy of image classification.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Engineering, Electrical & Electronic
Xinxin Shan, Yue Lu, Qingli Li, Ying Wen
Summary: The paper introduces a framework for partial face recognition based on model-based transfer learning and sparse coding, demonstrating its efficacy through experimental results.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Decheng Liu, Xinbo Gao, Chunlei Peng, Nannan Wang, Jie Li
Summary: The article explores learning interpretable representations for complex heterogeneous faces and proposes the HFIDR and M-HFIDR methods for cross-modality recognition and synthesis tasks, achieving efficiency in face recognition and synthesis.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(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
Computer Science, Artificial Intelligence
Jianwu Wan, Yinjuan Chen, Bing Bai
Summary: The paper proposes a unified cost-sensitive framework for face recognition, which significantly reduces the overall misclassification loss of face recognition system and classification errors associated with high costs, as demonstrated by experimental results.
PATTERN RECOGNITION
(2021)
Article
Geochemistry & Geophysics
Benqin Song, Peijun Li, Xiuping Jia
Summary: In this paper, two novel feature selection methods for one-class classification of remote sensing images using sparse representation were proposed. The methods, feature selection based on sample reconstruction (FSSR) and feature selection based on feature reconstruction (FSFR), were evaluated and compared with existing methods in two different case studies. Experimental results showed that both methods generally outperformed the existing ones.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2021)
Article
Computer Science, Artificial Intelligence
Qinglan Fan, Ying Bi, Bing Xue, Mengjie Zhang
Summary: This paper proposes a new genetic programming approach that combines feature extraction, feature construction, and classification, achieving high accuracy in both binary and multi-class image classification tasks. The approach outperforms benchmark methods on multiple datasets, and the evolved GP programs show appropriate tree sizes and interpretability.
APPLIED SOFT COMPUTING
(2022)
Article
Optics
Morteza Najmabadi, Payman Moallem
Summary: This paper introduces a novel approach called LSDP for face recognition, which encodes facial textures based on gradient information in a simple and compact way. Experimental results demonstrate that LSDP achieves higher recognition rates compared to other methods under different evaluation protocols, especially in challenging conditions with low dimensions of the feature space or fewer training samples.
Article
Computer Science, Artificial Intelligence
Dongmei Mo, Zhihui Lai, Jie Zhou, Hu Qinghua
Summary: In this paper, a new method called Jointly Sparse Orthogonal Linear Discriminant Analysis (JSOLDA) is proposed to improve the performance of linear discriminant analysis in the field of computer vision and pattern recognition. The proposed method obtains the sparse orthogonal projections for feature extraction through constrained scatter matrix decomposition. Experimental results demonstrate that JSOLDA outperforms several well-known methods based on linear discriminant analysis and L2,1-norm.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Information Systems
Lucia Cimmino, Michele Nappi, Fabio Narducci, Chiara Pero
Summary: This study explores the impact of wearing masks on face recognition and proposes a robust recognition approach for mobile devices by analyzing the spatio-temporal features of the periocular region. Machine learning techniques are used to classify and analyze the periocular region, and the experimental results show promising performance.
Article
Multidisciplinary Sciences
Michael M. Abdel-Sayed, Ahmed Khattab, Mohamed F. Abu-Elyazeed
JOURNAL OF ADVANCED RESEARCH
(2016)
Article
Computer Science, Information Systems
Michael M. Abdel-Sayed, Ahmed Khattab, Mohamed F. Abu-Elyazeed
Article
Computer Science, Artificial Intelligence
Michael Melek, Mohamed F. Abu-Elyazeed, Ahmed Khattab
Summary: A new framework for iris recognition with high efficiency and speed is presented, utilizing Gabor features and supervised locality-preserving projections with heat kernel weights for feature extraction, as well as sparse representation-based classification to significantly improve recognition rate and performance.
Article
Telecommunications
Michael Melek, Ahmed Khattab, Mohamed F. Abu-Elyazeed
IET WIRELESS SENSOR SYSTEMS
(2020)
Proceedings Paper
Computer Science, Theory & Methods
Michael M. Abdel-Sayed, Ahmed Khattab, Mohamed F. Abu-Elyazeed
PROCEEDINGS OF 2018 INTERNATIONAL CONFERENCE ON INNOVATIVE TRENDS IN COMPUTER ENGINEERING (ITCE' 2018)
(2018)
Proceedings Paper
Engineering, Electrical & Electronic
Michael M. Abdel-Sayed, Ahmed Khattab, Mohamed F. Abu-Elyazeed
2017 PROCEEDINGS OF THE JAPAN-AFRICA CONFERENCE ON ELECTRONICS, COMMUNICATIONS, AND COMPUTERS (JAC-ECC)
(2017)
Proceedings Paper
Engineering, Electrical & Electronic
Michael M. Abdel-Sayed, Ahmed Khattab, Mohamed F. Abu-Elyazeed
2016 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
(2016)