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Computer Science, Artificial Intelligence
Fan Liu, Delong Chen, Fei Wang, Zewen Li, Feng Xu
Summary: This paper focuses on deep learning-based methods for single sample face recognition. It categorizes them into virtual sample methods and generic learning methods, analyzing them in detail. The paper also reviews commonly used face datasets and compares the results of different types of models. Problems with existing methods are discussed, and future development directions are proposed.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Mathematics
Yuhua Ding, Zhenmin Tang, Fei Wang
Summary: This paper presents a single-sample face recognition method based on a shared generative adversarial network. By generating and expanding the gallery dataset, and utilizing a deep convolutional neural network for feature extraction and classifier training, it can effectively recognize single-sample faces.
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Chemistry, Analytical
Insaf Adjabi, Abdeldjalil Ouahabi, Amir Benzaoui, Sebastien Jacques
Summary: This paper introduces an original method for Single-Sample Face Recognition (SSFR) called Multi-Block Color-Binarized Statistical Image Features (MB-C-BSIF), which achieves superior results in unconstrained environments by utilizing various facial characteristics. The method decomposes facial images into color channels, selects local features, and uses a distance measurement of the K-nearest neighbors classifier for identity determination. Extensive experiments demonstrate the method's competitiveness and efficiency in real-time identification.
Article
Computer Science, Software Engineering
Zhengqi Zhang, Li Zhang, Meng Zhang
Summary: The paper introduces a dissimilarity-based nearest neighbor classifier (DNNC) for single-sample face recognition tasks, which computes the dissimilarities between test image patch sets and training image patch sets to determine the category of test images. Extensive experiments show the effectiveness of DNNC, especially in cases of obscuration, in the AR face database.
Article
Computer Science, Artificial Intelligence
Yimin Wen, Haiyang Yi, Zhigang Fan, Zhi Xu, Yun Xue, Yujian Li
Summary: This paper introduces a new method, Gallery-Sensitive Single Sample Face Recognition based on Domain Adaptation (GS-DA), which effectively leverages an unlabeled target training dataset, a labeled source training dataset, and a gallery dataset to enhance the performance of SSFR.
Article
Computer Science, Artificial Intelligence
Meng Pang, Yiu-Ming Cheung, Qiquan Shi, Mengke Li
Summary: This article focuses on the problem of single-sample per person face recognition with a contaminated biometric enrolment database. The proposed IDGL method uses a semisupervised low-rank representation framework to recover prototypes and learn a representative variation dictionary, improving label estimation accuracy iteratively through a dynamic learning network. Experimental results have shown the superiority of IDGL over state-of-the-art counterparts.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Computer Science, Information Systems
Meng-Jun Ye, Chang-Hui Hu, Li-Guang Wan, Gai-Hui Lei
Summary: The paper introduces a fast extended sparse representation classification method that significantly improves the computation efficiency of ESRC through strategies based on positive sparse coefficients and large positive sparse coefficients. Experimental results demonstrate the high utility and feasibility of this method in face recognition systems.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Hardware & Architecture
Fan Liu, Fei Wang, Yu Wang, Jun Zhou, Feng Xu
Summary: In this study, a cycle-autoencoder model is proposed to generate and remove facial variations in single sample face recognition. The approach demonstrates effectiveness and robustness through the use of cycle consistency scheme and block-sparse joint representation method.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Xiao Luan, Xin Wang, Linghui Liu, Weisheng Li
Summary: Single sample per person (SSPP) has always been a challenging problem for face recognition due to limited information and facial variations. Existing methods have achieved success, but they degrade when accompanied by a contaminated gallery database. To solve this, a multi-level dynamic error coding method is proposed, which demonstrates strong generalization ability to dictionaries and is robust against facial variations under SSPP.
PATTERN RECOGNITION LETTERS
(2023)
Article
Computer Science, Artificial Intelligence
Vivek Tomar, Nitin Kumar, Ayush Raj Srivastava
Summary: Face recognition is widely applied in various domains, especially in the challenging scenario of single sample face recognition problem. The complexity increases when there are variations in illumination, pose, occlusion, and expression. Deep learning methods have shown comparable performance to humans, enabling accurate recognition even with a single sample. This paper presents a comprehensive survey of single sample face recognition using deep learning, with a novel taxonomy dividing methods into virtual sample generation, feature-based, and hybrid methods. Performance comparison and future research directions are also discussed.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Jian Zou, Yue Zhang, Hongjian Liu, Lifeng Ma
Summary: This paper presents a novel method for single sample face recognition using grayscale monogenic features and kernel sparse representation on multiple Riemannian manifolds. The approach involves extracting local features from different regions of the face images, modeling the corresponding feature vectors as points on a Grassmann manifold, extracting co-occurrence distributions of feature images, and training a kernel sparse representation classifier using multiple kernel fusion. Experimental results demonstrate the superiority of the proposed method.
Article
Multidisciplinary Sciences
Irina Higgins, Le Chang, Victoria Langston, Demis Hassabis, Christopher Summerfield, Doris Tsao, Matthew Botvinick
Summary: The study explores how the brain processes facial recognition by using deep learning techniques, revealing that the brain disentangles facial images into semantically meaningful factors such as age or smile. Through a deep self-supervised generative model, beta-VAE, the researchers model neural responses in the macaque IT cortex to faces, demonstrating a strong correspondence between generative factors and single IT neurons. This suggests that optimizing disentangling objectives may lead to representations that closely resemble those in the IT at the single unit level.
NATURE COMMUNICATIONS
(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
Computer Science, Artificial Intelligence
Yongjun Zhang, Wenjie Liu, Haisheng Fan, Yongjie Zou, Zhongwei Cui, Qian Wang
Summary: Dictionary learning has become a research hotspot, and constructing a robust dictionary is a key issue, especially in face recognition scenarios. This study proposes a method to improve robustness by generating virtual samples and designing a fusion classification scheme, showing superior results compared to existing algorithms.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Jacinto Rivero-Hernandez, Annette Morales-Gonzalez, Lester Guerra Denis, Heydi Mendez-Vazquez
Summary: This paper proposes a novel adaptive aggregation scheme based on ordered weighted average operators for video face recognition, and develops two different concrete implementations to validate its suitability, achieving competitive results in accuracy.
PATTERN RECOGNITION LETTERS
(2021)
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Ammar Hawbani, Xingfu Wang, Saleem Karmoshi, Lin Wang, Naji Husaini
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(2017)
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Chunhui Ding, Zhengwei Hu, Saleem Karmoshi, Ming Zhu
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(2017)
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WIRELESS COMMUNICATIONS & MOBILE COMPUTING
(2017)
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Computer Science, Information Systems
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Computer Science, Information Systems
Ammar Hawbani, Xingfu Wang, Yaser Sharabi, Aiman Ghannami, Hassan Kuhlani, Saleem Karmoshi
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2019)
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Computer Science, Information Systems
Shuo Wang, Saleem Karmoshi, Fekri Saleh, Naji Alhusaini, Jing Li, Ming Zhu, Ammar Hawbani
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Computer Science, Information Systems
Naji Alhusaini, Saleem Karmoshi, Ammar Hawbani, Li Jing, Abdullah Alhusaini, Yaser Al-Sharabi
Proceedings Paper
Computer Science, Theory & Methods
Saleem Karmoshi, Wang Shuo, Fekri Saleh, Jing Li, Ming Zhu
2019 THE 3RD INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPILATION, COMPUTING AND COMMUNICATIONS (HP3C 2019)
(2019)
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Computer Science, Artificial Intelligence
Tianlong Bao, Binquan Wang, Saleem Karmoshi, Chenglin Liu, Ming Zhu
INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL
(2019)
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Computer Science, Software Engineering
Chunhui Ding, Tianlong Bao, Saleem Karmoshi, Ming Zhu
2017 IEEE 9TH INTERNATIONAL CONFERENCE ON COMMUNICATION SOFTWARE AND NETWORKS (ICCSN)
(2017)
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Tianlong Bao, Chunhui Ding, Saleem Karmoshi, Ming Zhu
SECOND INTERNATIONAL WORKSHOP ON PATTERN RECOGNITION
(2017)