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arXiv 2025年

作者： Yang, Ziyuan Chen, Yingyu Gao, Chengrui Teoh, Andrew Beng Jin Zhang, Bob Zhang, Yi the College of Computer Science Sichuan University Chengdu610065 China Singapore the School of Electrical and Electronic Engineering College of Engineering Yonsei University Seoul Korea Republic of the Pattern Analysis and Machine Intelligence Group Department of Computer and Information Science University of Macau Taipa China the School of Cyber Science and Engineering Sichuan University Chengdu610065 China

Current deep learning (DL)-based palmprint verification models rely on centralized training with large datasets, which raises significant privacy concerns due to biometric data’s sensitive and immutable nature. Federated learning (FL), a privacy-preserving distributed learning paradigm, offers a compelling alternative by enabling collaborative model training without the need for data sharing. However, FL-based palmprint verification faces critical challenges, including data heterogeneity from diverse identities and the absence of standardized evaluation benchmarks. This paper addresses these gaps by establishing a comprehensive benchmark for FL-based palmprint verification, which explicitly defines and evaluates two practical scenarios: closed-set and open-set verification. We propose FedPalm, a unified FL framework that balances local adaptability with global generalization. Each client trains a personalized textural expert tailored to local data and collaboratively contributes to a shared global textural expert for extracting generalized features. To further enhance verification performance, we introduce a Textural Expert Interaction Module that dynamically routes textural features among experts to generate refined side textural features. Learnable parameters are employed to model relationships between original and side features, fostering cross-texture-expert interaction and improving feature discrimination. Extensive experiments validate the effectiveness of FedPalm, demonstrating robust performance across both scenarios and providing a promising foundation for advancing FL-based palmprint verification research. The related code will be publicly available upon acceptance at https://***/Zi-YuanYang/FedPalm. Copyright © 2025, The Authors. All rights reserved.

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Deep Learning in Palmprint Recognition-A Comprehensive Survey

arXiv

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arXiv 2025年

作者： Gao, Chengrui Yang, Ziyuan Jia, Wei Leng, Lu Zhang, Bob Teoh, Andrew Beng Jin College of Computer Science Sichuan University Chengdu610065 China Singapore School of Computer and Information Hefei University of Technology Hefei China Jiangxi Provincial Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China Pattern Analysis and Machine Intelligence Group Department of Computer and Information Science University of Macau Taipa China School of Electrical and Electronic Engineering College of Engineering Yonsei University Seoul Korea Republic of

Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as they heavily depend on researchers' prior knowledge. Deep learning (DL) has been introduced to address this limitation, leveraging its remarkable successes across various domains. While existing surveys focus narrowly on specific tasks within palmprint recognition-often grounded in traditional methodologies-there remains a significant gap in comprehensive research exploring DL-based approaches across all facets of palmprint recognition. This paper bridges that gap by thoroughly reviewing recent advancements in DL-powered palmprint recognition. The paper systematically examines progress across key tasks, including region-of-interest segmentation, feature extraction, and security/privacy-oriented challenges. Beyond highlighting these advancements, the paper identifies current challenges and uncovers promising opportunities for future research. By consolidating state-of-the-art progress, this review serves as a valuable resource for researchers, enabling them to stay abreast of cutting-edge technologies and drive innovation in palmprint recognition. Copyright © 2025, The Authors. All rights reserved.

关键词： Biometrics

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CLASSIFICATION OF IMPEDANCE TRACES

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS 1987年第4期17卷 707-713页

作者： SHEN, HC PILKEY, RM Department of Systems Design Engineering Pattern Analysis and Machine Intelligence Group University of Waterloo Waterloo ONT Canada

In the field of electronic fault detection much of the evaluation is performed by a human operator using a visual inspection system. One such system displays the current versus voltage (I-V) characteristics, referred to as the impedance trace, of the electronic component under test. The shape of the impedance trace indicates the integrity of the component. The subjectivity and poor repeatability of the visual inspection have been identified as major deficiencies in the existing systems. A microprocessor-based system has been proposed to store and provide a quantitative classification of the impedance traces. The primary objective of this system is to provide an efficient automated system for classifying the impedance traces of different electronic components. Thus three trace representations and two classification schemes are proposed, based upon techniques developed in the field of pattern recognition. An experiment contrasts the different classification approaches. A Fourier series representation, combining both magnitude and phase features, was determined to provide the best overall performance when used in conjunction with a developed distance measure.

关键词： Impedance Inspection Electronic components Fault detection Performance evaluation Humans Displays Voltage Electronic equipment testing System testing

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Towards better outliers detection for gene expression datasets

Towards better outliers detection for gene expression datase...

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International Conference on Biocomputation, Bioinformatics, and Biomedical Technologies, BIOTECHNO 2008

作者： Kashef, R. Kamel, M.S. University of Waterloo Electrical and Computer Engineering Department Pattern Analysis and Machine Intelligence Group

ISBN: (纸本)9780769531915

This paper compares the performance of three clustering algorithms on the task of outlier's detection. The goal is to illustrate that better clustering indicates better detection of outliers, k-means (KM) , Bisecting k-means (BKM) and the Partitioning Around Medoids (PAM) algorithms are each combined with the clustering-based outliers detection (FindCBLOF) method. Undertaken experimental results over four gene expression datasets where outliers are presented show that the clustering solutions of the PAM algorithm enable the FindCBLOF algorithm to discover more outliers than those of both the k-means and the bisecting k-means algorithms. The main reason for this is that PAM provides better clustering quality than that of the other two clustering algorithms on the tested datasets measured by external and internal quality measures. © 2008 IEEE.

关键词： Clustering algorithms

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Sparsity-Induced Graph Convolutional Network for Semisupervised Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial intelligence 2021年第6期2卷 549-564页

作者： Zhou, Jianhang Zeng, Shaoning Zhang, Bob The Pattern Analysis and Machine Intelligence Research Group Department of Computer and Information Science University of Macau 999078 China of University of Electronic Science and Technology of China Zhejiang 313000 China

The graph representation (GR) in a data space reveals the intrinsic information as well as the natural relationships of data, which is regarded as a powerful means of representation for solving the semisupervised learning problem. To effectively learn on a predefined graph with both labeled data and unlabeled data, the graph convolutional network (GCN) was proposed and has attracted a lot of attention due to its high-performance graph-based feature extraction along with its low computational complexity. Nevertheless, the performance of GCNs is highly sensitive to the quality of the graph, meaning with high probability, the GCNs will achieve poor performances on a badly defined graphs. In numerous real-world semisupervised learning problems, the graph connecting each entity in the data space implicitly exists so that there is no naturally predefined graph in these problems. To overcome the issues, in this article, we apply unified GR techniques and GCNs in a framework that can be implemented in semisupervised learning problems. To achieve this framework, we propose sparsity-induced graph convolutional network (SIGCN) for semisupervised learning. SIGCN introduces the sparsity to formulate significant relationships between instances by constructing a newly proposed L0-based graph (termed as the sparsity-induced graph) before applying graph convolution to capture the high-quality features based on this graph for label propagation. We prove and demonstrate the feasibility of the unified framework as well as effectiveness in capturing features. Extensive experiments and comparisons were performed to show that the proposed SIGCN obtains a state-of-the-art performance in the semisupervised learning problem. © 2021 IEEE.

关键词： Convolution

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An efficient method for tagging a query with category labels using wikipedia towards enhancing search engine results

An efficient method for tagging a query with category labels...

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2010 IEEE/WIC/ACM International Conference on Web intelligence, WI 2010

作者： Alemzadeh, Milad Karray, Fakhri Pattern Analysis and Machine Intelligence Research Group Electrical and Computer Engineering Department University of Waterloo Waterloo Canada

ISBN: (纸本)9780769541914

This paper intends to present a straightforward, extensive, and noise resistant method for efficiently tagging a web query, submitted to a search engine, with proper category labels. These labels are intended to represent the closest categories related to the query which can ultimately be used to enhance the results of any typical search engine by either restricting the results to matching categories or enriching the query itself. The presented method effectively rules out noise words within a query, forms the optimal keyword packs using a density function, and returns a set of category labels which represent the common topics of the given query using Wikipedia category hierarchy. © 2010 IEEE.

关键词： Search engines

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NOISE HOMOGENIZATION VIA MULTI-CHANNEL WAVELET FILTERING FOR HIGH-FIDELITY SAMPLE GENERATION IN GANS

NOISE HOMOGENIZATION VIA MULTI-CHANNEL WAVELET FILTERING FOR...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Zeng, Shaoning Zhang, Bob University of Electronic Science and Technology of China China Pattern Analysis and Machine Intelligence Research Group Department of Computer and Information Science University of Macau China

ISBN: (纸本)9781665438643

In a typical Generative Adversarial Network (GAN), a noise is sampled to generate fake samples via a series of convolutional operations after random initialization. However, current GANs merely rely on the pixel space to sample the noise, increasing the difficulty of approaching the target distribution. Fortunately, the long proven wavelet transformation is able to decompose multiple spectral information from the images. In this work, we propose a novel multi-channel wavelet-based filtering method for GANs, to cope with this problem. The proposed WaveletNet embeds a wavelet deconvolution layer in the generator to take advantage of the wavelet deconvolution. By learning a filter with multiple channels, or multiple convolutional filters, it can efficiently homogenize the sampled noise via an averaging operation, to generate high-fidelity samples. We conducted benchmark experiments on the Fashion-MNIST, KMNIST, and SVHN datasets through an open GAN benchmark tool. The results showed that WaveletGAN has excellent performance in generating high-fidelity samples. © 2021 IEEE

关键词： Generative adversarial networks

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Representing Discovered patterns Using Attributed Hypergraph 2

Representing Discovered Patterns Using Attributed Hypergraph

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2nd International Conference on Knowledge Discovery and Data Mining, KDD 1996

作者： Wang, Yang Wong, Andrew K.C. Department of Systems Design Engineering Pattern Analysis and Machine Intelligence Group University of Waterloo WaterlooONN2L 3G1 Canada

ISBN: (纸本)1577350049

One of the fundamental problems in knowledge discovery in databases and other applications of AI is how to represent knowledge and *** representation schemes have various *** this paper, we propose a new knowledge representation scheme using attributed hypergraph (AHG), which is simple yet general enough to directly encode different order patterns discovered from large *** AHG, both the qualitative and quantitative relations are represented as attributed *** representation is lucid and transparent for ***, patterns in AHG are easy to *** the discussion, some basic manipulations of AHG for data mining tasks are briefly *** paper ends with examples of pattern representation using AHG. © 1996 AAAI (***). All Rights Reserved.

关键词： Knowledge representation

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Skew estimation and correction for form documents using wavelet decomposition

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2nd International Conference on Image analysis and Recognition, ICIAR 2005

作者： Xi, Dihua Kamel, Mohamed Lee, Seong-Whan Pattern Analysis and Machine Intelligence Research Group Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Center for Artificial Vision Research Korea University Seoul Korea Republic of

ISBN: (纸本)3540290699

Form document image processing has become an increasingly essential technology in office automation tasks. One of the problems is that the document image may appear skewed for many reasons. Therefore, the skew estimation plays an important role in any automatic document analysis system. In the past few years, many algorithms have been developed to detect the skew angle of text document images. However, these algorithms suffer from two major deficiencies. Firstly, most of them suppose that the original image is monochrome and therefore they are not suitable to apply to documents with a complicated background. Secondly, most of the current methods were developed for general document images that are not as complicated as form documents. In this paper, we present a new approach to skew detection for grey-level form document images. In our system, image decomposition by 2D wavelet transformations is used to estimate the skew angle. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Image processing

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Robotic vision: 3D object recognition and pose determination

Robotic vision: 3D object recognition and pose determination

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IEEE International Workshop on Intelligent Robots and Systems (IROS)

作者： A.K.C. Wong L. Rong X. Liang Pattern Analysis and Machine Intelligence Group University of Waterloo Waterloo ONT Canada

A challenge in 3D computer vision is to automatically acquire 3D models of objects through a CCD camera and to use the acquired models to recognize objects and estimate their poses. The PAMI System works on images acquired from a single CCD camera. It first detects salient features from an image and then groups them according to their types as well as their spatial, geometrical and topological relations. The feature grouping types include: a) four corner points and triplets of lines forming corners; b) curve segments fitted into ellipses. The use of matching hypotheses generated based on feature groupings is usually more robust and effective than the combinatorial matching of point features.

关键词： Robot vision systems Object recognition Image edge detection Feature extraction Computer vision Image segmentation Charge coupled devices Charge-coupled image sensors Robustness machine vision

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