With the development of deep learning, significant progress has been made in change detection (CD) methods for remote sensing (RS) images. However, many convolutional neural network (CNN)-based methods are constrained in capturing long-range dependencies due to the limitations of the receptive field. Transformers rely on self-attention mechanisms to effectively achieve global information modeling and are widely used in CD tasks. Nevertheless, transformer-based CD methods still suffer from issues such as pseudochanges and incomplete edges due to the lack of position and temporal correlations in bitemporal RS images. To deal with this issue, we propose a position-temporal awareness transformer (PT-Former), which models position and temporal relations in bitemporal images. Specifically, a Siamese network attached to a position-aware embedding module (PEM) serves as a feature encoder to extract the features of changed areas. Then, a temporal difference perception module (TDPM) is designed to capture the cross-temporal shift and enhance the difference perception ability during cross-temporal interaction. Meanwhile, the contextual information of the ground object is aggregated by the fusion block, and the spatial relation is reconstructed under the guidance of bitemporal features. The experimental results validate the superiority of PT-Former on three benchmark datasets, including the season-varying CD (SVCD) dataset, the learning vision and RS laboratory building CD (LEVIR-CD) dataset, and the WHU-CD dataset confirming the potential of PT-Former for CD tasks in RS images. The code will be available athttps://github.com/liuyk29/PT-Former.

In recent years, Electrocardiogram (ECG) biometrics has gained extensive attention. However, most existing methods adopted offline batch learning, which means that they need to accumulate all data and retrain the model when new data comes. Therefore, it is inefficient and unpractical for them to handle the online scenario where new data may continually come. To overcome the above limitation, we propose a novel ECG biometrics framework, termed Online ECG Biomet-rics based on Hadamard Codes. Firstly, we leverage matrix factorization to learn discriminative representations for ECG signals from their base feature space. Considering to leverage the orthogonal property of the Hadamard matrix, we use it to construct Hadamard codes to represent individuals and further guide the learning of representations. Furthermore, we develop an online optimization algorithm, which is efficient and effective to investigate the incremental problem in the context of ECG biometrics. The experimental results on two benchmark datasets indicate the merits of the proposed framework over the state-of-the-art.

Electrocardiogram (ECG) biometrics has aroused extensive attention in the research field of biometric recognition. How-ever, most existing methods either only consider a single do-main (time domain or frequency domain) to extract features or extract multi-features while ignoring the specific proper-ties of each domain. In this paper, we propose a novel ECG biometrics framework termed Joint Dual-domain Matrix Factorization (JDMF). JDMF learns latent spaces for each do-main by exploring the cross-correlations between them and preserving domain-specific properties. To endow the latent spaces with more powerful representation capabilities, JDMF further makes full use of the supervised information and could automatically learn the weights of domains. The experimental results on two widely-used datasets indicate that the proposed framework can outperform state-of-the-arts.

Electrocardiogram (ECG) biometrics has been an emerging field, and off-the-person ECG biometrics capturing the ECG from fingertips is one of the new trends in this field. However, dynamic morphological variability in the same person and low signal-to-noise ratios pose great challenges for off-the-person ECG biometrics. To reduce the dynamic morphological variability, this paper introduces deep metric learning into ECG biometrics to learn intra-individual compact features. To enforce the robust of proposed method, dual-domain features extracted from both 1D signals and 2D spectrograms are integrated by low-rank fusion. Furthermore, this method dispenses with the need for noise removal and outliers discarding completely. Experiments on two off-the-person ECG benchmark databases demonstrate that the proposed method significantly outperforms the state-of-the-art methods. Additionally, ablation experiments show the effectiveness of every part of our framework.

Semantic segmentation for remote sensing is a crucial but challenging task. Many supervised semantic segmentation methods rely heavily on a large-scale pixel-wise annotated data set, but it is time-consuming and laborious to provide manual annotation. However, due to the common domain shift of remote sensing images, a direct transfer might not perform well. Therefore, many unsupervised domain adaptation methods have been proposed to solve the data distribution discrepancy in remote-sensing data sets, but these methods cannot completely utilize the features extracted in the training process. In addition, the correlations between feature map channels are crucial for the pixel-wise classification task. In this letter, a covariance-based channel attention module is proposed to capture correlations by covariance metric and weighting the feature map channels. To further improve the domain adaptation performance, we propose a three-stage unsupervised domain adaptation semantic segmentation method for remote-sensing images, we fine-tune the model which has been trained on the source domain on the target domain via self training and knowledge distillation. To test the effectiveness of the proposed method, experiments are conducted on the ISPRS 2-D Semantic Labeling data set and an urban drone data set. Our method shows a better performance advantage compared with other state-of-the-art methods.

ECG biometric recognition has received plenty of attention in biometrics area. In recent years, various classical sparse representation and dictionary learning methods have been utilized in ECG biometric recognition. However, to produce better classification results, lP-norm is used to regularize the representation coefficients, which undoubtedly brings time cost problem. To overcome this limitation, our method, namely label-guided dictionary pair learning, aims to learn a projective dictionary and reconstructed dictionary jointly, which achieves signal representation and reconstruction simultaneously. Introduction of label information with each dictionary item and Fisher-like regularization on projective dictionary enforce discriminability during the dictionary learning process. Alternating direction method of multipliers is then exploited to optimize the corresponding objective function. Extensive experiments on two databases demonstrate that our method can achieve better performance compared with state-of-the-art ECG biometric recognition methods.

Electrocardiogram (ECG) biometric recognition has recently attracted considerable attention and various promising approaches have been proposed. However, due to the real nonstationary ECG noise environment, it is still challenging to perform this technique robustly and precisely. In this paper, we propose a novel ECG biometrics framework named robuSt semanTic spacE leaRning with Local sImilarity preserviNG (STERLING) to learn a latent space where ECG signals can be robustly and discriminatively represented with semantic information and local structure being preserved. Specifically, in the proposed framework, a novel loss function is proposed to learn robust semantic representation by introducing l2,1-norm loss and making full use of the supervised information. In addition, a graph regularization is imposed to preserve the local structure information in each subject. Finally, in the learnt latent space, matching can be effectively done. The experimental results on three widely-used datasets indicate that the proposed framework can outperform the state-of-the-arts.

Devices such as smartphones and tablets have spurred interest in off-the-person electrocardiogram (ECG) biometric recognition. While the advantage of using multi-feature information for establishing identities has been widely recognized, computational sparse representation models for multi-feature biometric recognition have only recently received more attention. We propose a unified sparse representation framework which collaboratively exploits joint and specific patterns for ECG biometric recognition. In particular, unlike joint sparse representation, which only considers the consistency among sparsity patterns of multiple features, we combine the consistent and pairwise constraints, which not only learn latent discriminant representations for all features but capture the interactions between them. In addition, our framework is universal and easily adapts to other multi-feature sparse representation models by just tuning the regularization parameters. The optimization problem is solved by an efficient alternating direction method of multipliers (ADMM). Extensive experiments on two publicly available off-the-person datasets demonstrate that our method can achieve competitive or even superior performance compared to state-of-the-art ECG biometric recognition methods.

Vein pattern-based methods have powerfully promoted the performance of finger vein recognition. However, it is not easy to precisely extract vein patterns from images, especially from low-quality images, and the non-vein area have been proved to be helpful for recognition. This paper proposes to use low-rank representation to extract as much noiseless discriminative information as possible from finger vein images. However, image deformation and image quality variations weaken the correlation of genuine images, and therefore damage the low-rank linear representation. To further deal with this problem, the class labels of training images and the local geometric structure between testing images and training images, reflected by sparse reconstruction errors of testing images, are used as constraints of low-rank coefficients. In particular, vein backbone decomposition based sparse representation is proposed to fast compute the deformation-robust reconstruction errors of each testing image. The reconstruction errors on sub-backbones of one training image are summed and modified as the constraint of the low-rank coefficient on this training image. We evaluate the proposed method on three widely used finger vein databases, and experimental results show that the proposed method performs well in finger vein recognition.

Although some image retrieval methods were proposed to accelerate finger vein recognition, the insufficient feature (e.g., the number of vein point) and unfavorable encoding (e.g., predefined threshold based binarization) limited retrieval performance largely. In view of this problem, we develop a new retrieval framework, based on the integration of discriminative texture features and similarity-preserving binary codes. In detail, the vector and scalar features, measuring the gray level, gray difference, and gray gathering of image patch, are both used to represent finger vein image. And to improve the retrieval efficiency, the high-dimensional decimal features are further encoded into the compact binary patterns by principal component analysis (PCA) and similarity-preserving iterative quantization (ITQ). Experimental results on one large finger vein database prove that the proposed method can powerfully improve the retrieval accuracy and efficiency.

Finger vein image retrieval is one significant technique for performing fast identification especially in large-scale applications. However, most existing retrieval methods were based on fixed-scale feature of non-overlapped rectangular image block, in which the representation ability of feature and the local consistency of vein pattern were both overlooked. And the weak encoding (e.g., predefined threshold based binarization) was also limited the retrieval performance. Focusing on these problems, this paper proposes a novel finger vein image retrieval framework based on similarity-preserving encoding of scale-varied superpixel feature. In the framework, locally consistent pixels in one superpixel are used as a unit of feature representation, and the feature length is varied with the category of the superpixel classified by the variance of lowest dimensional feature. Additionally, the feature compaction and feature rotation based encoding can minimize the quantization loss and preserve the similarity between the scale-varied feature and the encoded binary codes. Experimental results on six public finger vein databases demonstrate that the superiority of the proposed coding scale-varied superpixel feature based retrieval approach over the state-of-the-arts.

Finger vein pattern, as a promising hand-based biometric technology, has been well studied in recent years. In this paper, a new superpixel-based finger vein recognition method is presented. In the proposed method, we develop two types of effective superpixels, i.e. stable superpixel and discriminative superpixel to represent finger vein image and these superpixels are expected to play different roles in matching stage. In detail, the stable and discriminative superpixels are firstly learned from the training images for each enrolled class. When verifying a testing image, we just compare the superpixels at the same location as the two types of superpixels in template. Then, the two types of superpixels are combined utilizing a reversible weight-based fusion method in score level. Additionally, to further improve the recognition performance, we explore the superpixel context feature (SPCF). For each superpixel the SPCF is obtained by comparing the current superpixel with its surrounding neighbors. In the final matching stage, we integrate the matching score of two types of superpixels and it of the SPCF using the weighted SUM fusion method. The experimental results on two open finger vein databases, i.e. PolyU and SDUMLA-FV, show that our method not only performs better than the existing superpixel-based method, but also has advantages in comparison with some traditional ones.