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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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PDE-based Regularization of Orientation Field for Low-Quality Fingerprint images

PDE-based Regularization of Orientation Field for Low-Qualit...

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2012 IEEE 11th International Conference on Signal processing(ICSP 2012)

作者： Xiaoguang Chen Institute of Image Processing and Pattern Recognition North China University of Technology

ISBN: (纸本)9781467321969

Orientation field is one of the intrinsic charac-teristics of fingerprints, and it plays very important role in many processing phases of fingerprint recognition such as enhancement, minutiae extraction and matching. Therefore, accurate estimation of orientation filed is much necessary. In this paper, a novel PDE-based method is proposed for regularization of orientation field for low-quality fingerprint images. The method consists of four steps. Firstly, the coarse orientation field is computed using traditional gradient-based approach. Secondly, the reliability map of the orientation field is computed based on a procedure of multiscale coherence analysis. Then the orientation in the low-reliable region is reconstructed with the surrounding data by means of image inpainting technique. Finally, nonlinear diffusion filtering with adaptive diffusivity is performed on the whole orientation field. Experiments on the NIST SD4 fingerprint database indicated that the proposed algorithm is capable to estimate the orien-tation field accurately, especially for poor-quality fingerprints, and it can be integrated into fingerprint recognition systems to improve the performance.

关键词： Fingerprint recognition Orientation field PDE-based regularization

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General moving objects recognition method based on graph embedding dimension reduction algorithm

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Journal of Zhejiang university-Science A(Applied Physics & Engineering) 2009年第7期10卷 976-984页

作者： Yi ZHANG Jie YANG Kun LIU Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China

Effective and robust recognition and tracking of objects are the key problems in visual surveillance systems. Most existing object recognition methods were designed with particular objects in mind. This study presents a general moving objects recognition method using global features of targets. Targets are extracted with an adaptive Gaussian mixture model and their silhouette images are captured and unified. A new objects silhouette database is built to provide abundant samples to train the subspace feature. This database is more convincing than the previous ones. A more effective dimension reduction method based on graph embedding is used to obtain the projection eigenvector. In our experiments, we show the effective performance of our method in addressing the moving objects recognition problem and its superiority compared with the previous methods.

关键词： Moving objects recognition Adaptive Gaussian mixture model Principal component analysis Linear discriminant analysis Marginal Fisher analysis

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Bayesian moving object detection in dynamic scenes using an adaptive foreground model

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Journal of Zhejiang university-Science A(Applied Physics & Engineering) 2009年第12期10卷 1750-1758页

作者： Sheng-yang YU Fang-lin WANG Yun-feng XUE Jie YANG Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China

Accurate detection of moving objects is an important step in stable tracking or recognition. By using a nonparametric density estimation method over a joint domain-range representation of image pixels, the correlation between neighboring pixels can be used to achieve high levels of detection accuracy in the presence of dynamic background. However, color similarity between foreground and background will cause many foreground pixels to be misclassified. In this paper, an adaptive foreground model is exploited to detect moving objects in dynamic scenes. The foreground model provides an effective description of foreground by adaptively combining the temporal persistence and spatial coherence of moving objects. Building on the advantages of MAP-MRF (the maximum a posteriori in the Markov random field) decision framework, the proposed method performs well in addressing the challenging problem of missed detection caused by similarity in color between foreground and background pixels. Experimental results on real dynamic scenes show that the proposed method is robust and efficient.

关键词： Moving object detection Foreground model Kernel density estimation (KDE) MAP-MRF estimation

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Regularities of Formation of Ternary Intermetallic Compound between Transition Elements

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材料科学技术学报（英文版） 2001年第z1期17卷 S183-S185页

作者： Lixiu YAO Jie Yang Chenzhou YE Nianyi Chen Institute of Image Processing and Pattern Recognition Shanghai JiaoTong University Shanghai 200030 China

Four parameters, φ (electronegativity), nws1/3 (valence electron density in Wagner-Seitz cell),R (Pauling's metallic radius) and Z (number of valence electrons in atom), and the pattern recognition methods were used to investigate the regularities of formation of ternary intermetallic compounds between three transition elements. The obtained mathematical model expressed by some inequalities can be used as a criterion of ternary compound formation in "unknown" phase diagrams of alloy systems.

关键词： regularities of formation intermetallic compounds pattern recognition mathematical model electron density

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A CLASS OF C^1 DISCRETE INTERPOLANTS OVERTETRAHEDRA

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Journal of Computational Mathematics 1997年第2期15卷 149-158页

作者： X.C. Liu(institute of image processing and pattern recognition, Shanghai Jiao Tong university,Shanghai, china) Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

Smooth interpolants defined over tetrahedra are currently being developed for they have many applications in geography, solid modeling, finite element analysis, etc. In this paper, we will characterize a certain class of C-1 discrete tetrahedral interpolants with only C-1 data required. As special cases of the class characterized, we give two C-1 discrete tetrahedral interpolants which have concise expressions.

关键词： A CLASS OF C~1 DISCRETE INTERPOLANTS OVERTETRAHEDRA

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Average Weight Optimization RBPF Method for Target Tracking in Multi-Sensor Observations

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电子学报(英文版) 2013年第2期22卷 401-404页

作者： LIU Xianxing HU Zhentao LI Jie Institute of Image Processing and Pattern Recognition Henan University Kaifeng 475004 China

The reasonable design of particle filter framework in multi-sensor observation system is the key to expand the application domain of sampling nonlinear filters. Aiming at the effective realization of particle filter for multi-sensor target tracking problem, a novel average weight optimization Rao-Blackwellised particle filtering al- gorithm is proposed. Combining with the kinetic equation of target state evolution, RBPF is used as the basic es- timator of algorithm realization. For the rational utiliza- tion from multi-sensor observations and the reduction of the adverse influence from random observations noise in measuring process of particles weight, the average weight optimization strategy is used to improve the reliability and stability of particle weight variance. In addition, we give the concrete flow of RBPF in average weight optimization strategy. Finally, the theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.

关键词：多传感器重量优化平均重量观测噪声目标跟踪非线性滤波器粒子滤波优化策略

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An Improved Car-Following Model for Multiphase Vehicular Traffic Flow and Numerical Tests

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Communications in Theoretical Physics 2006年第2X期46卷 367-373页

作者： LI Zhi-Peng GONG Xiao-Bo LIU Yun-Cai Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200240 China

This paper attempts to introduce an improved difference model that modifies a car-following model, which takes the next-nearest-neighbor interaction into account. The hnprovement of this model over the previous one lies in that it performs more realistically in the dynamical motion for small delay time. The traffic behavior of the improved model is investigated with analytic and numerical methods with the finding that the new consideration could further stabilize traffic flow. And some simulation tests verify that the proposed model can demonstrate some complex physical features observed recently in real traffic such as the existence of three phases： free flow, coexisting flow, and jam flow; spontaneous formation of density waves; sudden flow drop in flow-density plane; traffic hysteresis in transition between the free and the coexisting flow. Furthermore, th.e improved model also predicts that the stable state to relative density in the coexisting flow is insusceptible to noise.

关键词： car-following models density waves traffic flow

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Practical Fast Computation of Zernike Moments

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Journal of Computer Science & technology 2002年第2期17卷 181-188页

作者： Al-Rawi Mohammed 杨杰 Image Processing and Pattern Recognition Institute Shanghai Jiaotong University Shanghai 200030 P.R. China

The fast computation of Zernike moments from normalized geometric moments has been developed in this paper. The computation is multiplication free and only additions are needed to generate Zernike moments. Geometric moments are generated using Hatamian's filter up to high orders by a very simple and straightforward computation scheme. Other kinds of moments (e.g., Legendre, pseudo Zernike) can be computed using the same algorithm after giving the proper transformations that state their relations to geometric moments. Proper normalizations of geometric moments are necessary so that the method can be used in the efficient computation of Zernike moments. To ensure fair comparisons, recursive algorithms are used to generate Zernike polynomials and other coefficients. The computational complexity model and test programs show that the speed-up factor of the proposed algorithm is superior with respect to other fast and/or direct computations. It perhaps is the first time that Zernike moments can be computed in real time rates, which encourages the use of Zernike momentfeatures in different image retrieval systems that support huge databases such as the XM experimental model stated for the MPEG-7 experimental core. It is concluded that choosing direct computation would be impractical.

关键词： Zernike moment fast algorithm digital filter invariant patternrecognition

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引用

Chinese Optics Letters 2003年第11期1卷 637-640页

作者：杜培军方涛唐宏施鹏飞 Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200030 Department of Spatial Informatics China University of Mining and Technology Xuzhou 221008 Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200030

In this paper, two new similarity measure methods based on set theory were proposed. Firstly, similarity measure of two sets based on set theory and set operation was discussed. This principle was used to spectral vectors, and two approaches were proposed. The first method was to create a spectral polygon corresponding to spectral curve, and similarity of two spectral vectors can be replaced by that of two polygons. Area of spectral polygon was used as quantification function and some effective indexes for similarity and dissimilarity were computed. The second method was to transform the original spectral vector to encoding vector according to absorption or reflectance feature bands, and similarity measure was conducted to encoding vectors. It proved that the spectral polygon-based approach was effective 'and can be used to hyperspectral RS image retrieval.

关键词： image processing Set theory

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