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
Computer Science, Artificial Intelligence
Yi Zhang, Keren Fu, Cong Han, Peng Cheng, Shanmin Yang, Xiao Yang
Summary: The proposed Pose-Guided Margin Loss (PGM-Face) and Pose-Guided Representation Transfer Network (PGRT-Net) enable learning more separable face features under arbitrary head poses in cross-pose face recognition, leading to improved performance compared to traditional methods.
Article
Computer Science, Artificial Intelligence
Kangli Zeng, Zhongyuan Wang, Tao Lu, Jianyu Chen, Zhen Han
Summary: In this paper, we propose an implicit spatial pose consistent transfer network (PCTN) to address the influence of pose variations on face recognition performance. The proposed method promotes the frontalization of tilted faces while preserving identity information, and achieves improved performance on benchmark datasets.
PATTERN RECOGNITION LETTERS
(2023)
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
Omar Elharrouss, Noor Almaadeed, Somaya Al-Maadeed, Fouad Khelifi
Summary: A multitask convolutional neural network method is proposed for face recognition under pose variations. The method combines pose estimation and face identification modules to recognize faces with different poses, and improves robustness through skin segmentation and pose estimation.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Rui Sun, Xiaoquan Shan, Fei Wang, Zhiguo Fan
Summary: This paper proposes a pose aligned modality-invariant feature learning method to improve accuracy in heterogeneous face recognition by disentangling the processing of face pose and modality into independent stages.
IMAGE AND VISION COMPUTING
(2023)
Article
Engineering, Electrical & Electronic
Yeda Yu, Xinyu Liu, Nian Liu, Boyu Chen, Tong Chen
Summary: This study developed an NIR face database with pose variations for research on pose-invariant face recognition. Experimental results showed that pose variations have a significant impact on recognition performance, and using the CNN-based method can improve recognition accuracy.
JOURNAL OF ELECTRONIC IMAGING
(2021)
Article
Computer Science, Theory & Methods
Philip Chikontwe, Yongbin Gao, Hyo Jong Lee
Summary: The study introduces a generative adversarial network architecture for pose-invariant face recognition, which achieves better results through an iterative warping scheme. Evaluations demonstrate the method's higher accuracy compared to prior methods.
MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Computer Science, Artificial Intelligence
M. Kas, Y. El-merabet, Y. Ruichek, R. Messoussi
Summary: This paper presents a new 2D PIFR technique based on Generative Adversarial Network image translation for improving face recognition performance when facing profile samples. By calculating the L1 distance between the generated image and the ground truth one, the proposed technique achieves a significant improvement of 33.57% compared to the baseline in the Combined-PIFR database evaluation.
INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL
(2022)
Article
Computer Science, Information Systems
Shinfeng D. Lin, Paulo E. Linares Otoya
Summary: This paper presents a novel approach to achieve pose-invariant face recognition using ensemble learning and local feature descriptors. The proposed method extracts feature vectors from image regions surrounding specific facial landmarks, and trains three different classification models as base learners. Experimental results show better performance compared to existing methods using the CMU-PIE dataset.
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
Xiaoyu Chen, Henry Y. K. Lau
Summary: This paper proposes an identity-level angular triplet loss for cross-age face recognition, which adjusts the angles between feature embeddings in an embedding space to represent similarities of images and improves the accuracy of recognition by learning discriminative features.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Amena Khatun, Simon Denman, Sridha Sridharan, Clinton Fookes
Summary: In this paper, an end-to-end pose-driven attention-guided generative adversarial network is proposed to generate multiple poses of a person. The attention mechanism is used to learn and transfer the subject pose, and a semantic-consistency loss is proposed to preserve the semantic information during pose transfer. Appearance and pose discriminators are utilized to ensure the realism and consistency of the transferred images. Incorporating the proposed approach in a person re-identification framework achieves realistic pose transferred images and state-of-the-art re-identification results.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Nayaneesh Kumar Mishra, Satish Kumar Singh
Summary: Loss functions are crucial in deep learning architectures for face recognition research. Hardmining loss, while effective, has a limitation of assigning near-zero loss values to easy examples, limiting their contribution in later training stages. To address this, a Regularized Hardmining loss is proposed to fine-tune the behavior of Hardmining loss for improved performance in face recognition accuracy. The application of Regularized Hardmining loss with Cross Entropy loss shows significant improvement in face recognition accuracy on the LFW dataset.
IMAGE AND VISION COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Chun-Hsien Lin, Wei-Jia Huang, Bing-Fei Wu
Summary: By proposing the deep representation alignment network (DRA-Net), this study aims to address pose variations in face recognition. The network utilizes a denoising autoencoder and deep representation transformation block for end-to-end training, implementing cosine loss and pairwise training to reduce the gap between frontal and profile representations. Experimental results show that DRA-Net outperforms other state-of-the-art methods, particularly for large pose angles across various benchmarks.
Article
Computer Science, Information Systems
Cheng Wang, Bingpeng Ma, Hong Chang, Shiguang Shan, Xilin Chen
Summary: This paper introduces a novel Bi-directional Task-Consistent Learning (BTCL) person search framework, which includes a Target-Specific Detector (TSD) and a re-ID model with Dynamic Adaptive Learning Structure (DALS) to meet the consistency needs between the detection and re-ID stages. Experimental results show that the framework achieves state-of-the-art performance on two widely-used person search datasets.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Article
Automation & Control Systems
Furong Xu, Bingpeng Ma, Hong Chang, Shiguang Shan
Summary: In this article, a novel method is proposed to address the dirty data quality and poor quantity issues in person reidentification, using weighted label correction and feature simulation techniques to improve model performance.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Shikang Yu, Hu Han, Shiguang Shan, Xilin Chen
Summary: This paper proposes a novel semi-supervised cross-modality synthesis method (CMOS-GAN) that can leverage both paired and unpaired face images to learn a robust cross-modality synthesis model. Experimental results show that the proposed method outperforms the state-of-the-art in three cross-modality face synthesis tasks.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2023)
Article
Computer Science, Artificial Intelligence
Wenbin Wang, Ruiping Wang, Shiguang Shan, Xilin Chen
Summary: This study proposes a method for modeling human perception based on scene graphs, which generates a hierarchically constructed scene graph to present the primary content first and then the secondary content on demand. The method represents the scene with a hierarchical structure and utilizes a hierarchical contextual propagation module to model the structural information. It further highlights the key relationships in the scene graph through relationship re-ranking. Experimental results demonstrate that the method achieves state-of-the-art performance in scene graph generation and excels at mining image-specific relationships.
INTERNATIONAL JOURNAL OF COMPUTER VISION
(2023)
Article
Computer Science, Artificial Intelligence
Yong Li, Shiguang Shan
Summary: In this study, a Meta Auxiliary Learning method (MAL) is proposed to automatically select highly related facial expression (FE) samples for better facial action unit (AU) detection performance. Experimental results demonstrate that MAL consistently improves AU detection performance compared with state-of-the-art multi-task and auxiliary learning methods.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2023)
Proceedings Paper
Computer Science, Cybernetics
Fei Chang, Jiabei Zeng, Qiaoyun Liu, Shiguang Shan
Summary: Analyzing gaze patterns is crucial for understanding communication. Current studies have mainly focused on detecting single patterns, such as mutual gaze or shared attention. This work redefines five static gaze patterns that cover all statuses during dyadic communication and proposes a network to recognize these mutually exclusive patterns from images. Experimental results demonstrate that our method outperforms other solutions on a benchmark for gaze pattern recognition. Our method also achieves state-of-the-art performance on two other single gaze pattern recognition tasks. Analysis of gaze patterns in preschool children confirms findings in psychology.
ACM SYMPOSIUM ON EYE TRACKING RESEARCH & APPLICATIONS, ETRA 2023
(2023)
Article
Computer Science, Artificial Intelligence
Yong Li, Shiguang Shan
Summary: In this paper, the problem of insufficient AU annotations in facial action unit (AU) detection is addressed by learning AU representations from unlabelled facial videos through contrastive learning. The proposed method achieves frame-wise discriminative AU representations within a short video clip and consistent AU representations for facial frames with analogous AUs sampled from different identities. Experimental results demonstrate the discriminative power of the learned AU representation for AU detection.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2023)
Article
Computer Science, Artificial Intelligence
Difei Gao, Ruiping Wang, Shiguang Shan, Xilin Chen
Summary: Inferring on visual facts and commonsense is crucial for an advanced VQA system. This requires models to surpass literal understanding of commonsense and fully ground it to the visual world. To evaluate this ability comprehensively, we introduce a VQA benchmark called CRIC, which includes new types of questions and an evaluation metric integrating correctness of answering and commonsense grounding. We also propose an automatic algorithm to generate question samples from scene and knowledge graphs, and analyze VQA models on the CRIC dataset. Experimental results demonstrate the challenge of grounding commonsense to image regions and joint reasoning on vision and commonsense. The dataset is available at https://cricvqa.github.io.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Ruibing Hou, Hong Chang, Bingpeng Ma, Shiguang Shan, Xilin Chen
Summary: In this study, we propose Dual Compensation Residual Networks to tackle the challenge of learning generalizable representation and classifier for class-imbalanced data in data-driven deep models. We introduce dual Feature Compensation Module (FCM) and Logit Compensation Module (LCM) to alleviate overfitting, and Residual Balanced Multi-Proxies Classifier (RBMC) to mitigate underfitting. Experimental results demonstrate the effectiveness of our approach.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Yude Wang, Jie Zhang, Meina Kan, Shiguang Shan, Xilin Chen
Summary: Scribble-supervised semantic segmentation is a low-cost weakly supervised technique that diffuses the labeled region of scribble to narrow the supervision gap. However, there is an annotation bias and label preference that affects the model training. To address this, BLPSeg is proposed to balance the label preference and design a local aggregation module to alleviate its impact.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Xuyang Guo, Meina Kan, Tianle Chen, Shiguang Shan
Summary: This study proposes an efficient and controllable method for continuous and fine hair editing using sliding bars, while also supporting discrete editing through reference photos and user-painted masks.
COMPUTER VISION - ECCV 2022, PT XV
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Zheng Yuan, Jie Zhang, Shiguang Shan
Summary: This paper introduces a novel architecture called Adaptive Image Transformation Learner (AITL) to improve the transferability of adversarial examples. Unlike existing works that use fixed combinational transformations, AITL improves the attack success rates by adaptively selecting the most effective combination of image transformations.
COMPUTER VISION - ECCV 2022, PT V
(2022)
Article
Computer Science, Artificial Intelligence
Nan Kang, Hong Chang, Bingpeng Ma, Shiguang Shan
Summary: This work aims to improve the feature extractor and classifier for long-tailed recognition through contrastive pretraining and feature normalization. It proposes a new balanced contrastive loss and fast contrastive initialization for better pretraining performance. It also introduces a novel generalized normalization classifier that outperforms traditional classifiers. The unified framework achieves competitive performance on long-tailed recognition benchmarks while maintaining high efficiency.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yong Li, Lingjie Lao, Zhen Cui, Shiguang Shan, Jian Yang
Summary: This study proposes a method called GraphJigsaw for cartoon face recognition, which utilizes jigsaw puzzles and graph convolutional networks. The proposed method achieves better accuracy compared to other existing methods without requiring manual annotations or additional computational burden during inference.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2022)
Article
Computer Science, Artificial Intelligence
Xinqian Gu, Hong Chang, Bingpeng Ma, Shiguang Shan
Summary: The proposed Motion Feature Aggregation (MFA) method efficiently models and aggregates motion information in the feature map level for video-based re-identification (re-id), consisting of coarse-grained and fine-grained motion learning modules that can model motion information from different granularities and are complementary to each other.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(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)