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METHODS OF COMBINING MULTIPLE CLASSIFIERS AND THEIR APPLICATIONS TO HANDWRITING recognition

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS 1992年第3期22卷 418-435页

作者： XU, L KRZYZAK, A SUEN, CY Centre for Pattern Recognition and Machine Intelligence Concordia University Montreal QUE Canada

Method of combining the classification powers of several classifiers is regarded as a general problem in various application areas of pattern recognition, and a systematic investigation has been made. Possible solutions to the problem can be divided into three categories according to the levels of information available from the various classifiers. Four approaches are proposed based on different methodologies for solving this problem. One is suitable for combining individual classifiers such as Bayesian, k-NN and various distance classifiers. The other three could be used for combining any kind of individual classifiers. On applying these methods to combine several classifiers for recognizing totally unconstrained handwritten numerals, the experimental results show that the performance of individual classifiers could be improved significantly. For example, on the U.S. zipcode database, the result of 98.9% recognition with 0.90% substitution and 0.2% rejection can be obtained, as well as a high reliability with 95% recognition, 0% substitution and 5% rejection. These results compared favorably to other research groups in Europe, Asia, and North America.

关键词： Handwriting recognition pattern recognition Character recognition Speech recognition Hidden Markov models Remote sensing Classification algorithms Brain modeling Bayesian methods Databases

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METHOD OF COMBINING MULTIPLE EXPERTS FOR THE recognition OF UNCONSTRAINED HANDWRITTEN NUMERALS

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IEEE TRANSACTIONS ON pattern ANALYSIS AND machine intelligence 1995年第1期17卷 90-94页

作者： HUANG, YS SUEN, CY Centre for Pattern Recognition and Machine Intelligence Concordia University West Montreal QUE Canada

For pattern recognition, when a single classifier cannot provide a decision which is 100 percent correct, multiple classifiers should be able to achieve higher accuracy. This is because group decisions are generally better than any individual's. Based on this concept, a method called the ''Behavior-Knowledge Space Method'' was developed, which can aggregate the decisions obtained from individual classifiers and derive the best final decisions from the statistical point of view. Experiments on 46,451 samples of unconstrained handwritten numerals have shown that this method achieves very promising performances and outperforms voting, Bayesian, and Dempster-Shafer approaches.

关键词： UNCONSTRAINED HANDWRITING recognition COMBINATION OF MULTIPLE CLASSIFIERS EVIDENCE AGGREGATION BEHAVIOR KNOWLEDGE SPACE KNOWLEDGE MODELING

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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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SIR-HCL: Semantic-Inconsistency Reasoning and Hybrid Contrastive Learning for Efficient Cross-Emotion Anomaly Detection

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Liu, Xin Chen, Qiyan Cheung, Yiu-Ming Peng, Shu-Juan Huaqiao University Department of Computer Science Xiamen361021 China Hong Kong Baptist University Department of Computer Science SAR Hong Kong Hong Kong Xiamen Key Laboratory of Computer Vision and Pattern Recognition Xiamen361021 China Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Xiamen361021 China Huaqiao University Department of Artificial Intelligence Xiamen China Fujian Province University Key Laboratory of Computer Vision and Machine Learning Huaqiao University Xiamen361021 China

Cross-emotion anomaly detection is an emerging and challenging research topic in cognitive analysis field, which aims at identifying the abnormal emotion pair whose semantic patterns are inconsistent across different emotional modalities. To the best of our knowledge, this topic has yet to be well studied, which could potentially benefit lots of valuable cognitive applications such as autistic children diagnosis and criminal deception detection. To this end, this paper proposes an efficient cross-emotion anomaly detection approach via semanticinconsistency reasoning and hybrid contrastive learning (SIR-HCL), which is the first attempt to detect the anomalous emotional pairs across the audio-visual emotions. First, the proposed framework utilizes dual-branch network to obtain the deep emotional features in each modality, and then employs the shared residual block to derive the semantically compatible features. Subsequently, an efficient hybrid contrastive learning approach is designed to enlarge the semantic-inconsistency among abnormal emotional pair with different affective classes, while enhancing the semantic-consistency and increasing the feature correlation between normal emotional pair from the same affective class. At the same time, an efficient bidirectional learning scheme is employed to significantly improve the data utilization and a two-component Beta Mixture Model is adaptively utilized to reason the anomalous emotion pairs. Extensive experiments evaluated on two benchmark datasets show that the proposed SIR-HCL method can well detect the anomalous emotional pairs across audio-visual emotional data, and brings substantial improvements over the state-of-the-art competing methods. © 2016 IEEE.

关键词： Contrastive Learning

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Color Image Segmentation by Edge Linking and Region Grouping

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Journal of Shanghai Jiaotong university(Science) 2011年第4期16卷 412-419页

作者：王宁杨杰 Key Laboratory of Pattern Recognition and Machine Intelligence of Ministry of Education Shanghai Jiaotong University

A novel method toward color image segmentation is proposed based on edge linking and region grouping. Firstly,the edges extracted by the Canny detector are linked to form *** of the end points of edges is connected by a direct line to the nearest pixel on another edge segment within a sub-window.A new distance is defined based on the feature that the edge tends to preserve its original *** sampling the lines to the image,the image is over-segmented to labeled ***,the labeled regions are grouped both locally and globally.A decision tree is constructed to decide the importance of properties that affect the merging ***,the result is refined by user’s selection of regions that compose the desired object. Experiments show that the method can effectively segment the object and is much faster than the state-of-the-art color image segmentation methods.

关键词： color image segmentation edge linking region grouping

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Error-correcting output coding for the convolutional neural network for optical character recognition

Error-correcting output coding for the convolutional neural ...

引用

ICDAR2009 - 10th International Conference on Document Analysis and recognition

作者： Deng, Huiqun Stathopoulos, George Suen, Ching Y. Center for Pattern Recognition and Machine Intelligence Concordia University Canada

ISBN: (纸本)9780769537252

It is known that convolutional neural networks (CNNs) are efficient for optical character recognition (OCR) and many other visual classification tasks. This paper applies error-correcting output coding (ECOC) to the CNN for segmentation-free OCR such that: 1) the CNN target outputs are designed according to code words of length N;2) the minimum Hamming distance of the code words is designed to be as large as possible given N. ECOC provides the CNN with the ability to reject or correct output errors to reduce character insertions and substitutions in the recognized text. Also, using code words instead of letter images as the CNN target outputs makes it possible to construct an OCR for a new language without designing the letter images as the target outputs. Experiments on the recognition of English letters, 10 digits, and some special characters show the effectiveness of ECOC in reducing insertions and substitutions. © 2009 IEEE.

关键词： Optical character recognition

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BUILDING A NEW GENERATION OF HANDWRITING recognition SYSTEMS

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pattern recognition LETTERS 1993年第4期14卷 303-315页

作者： SUEN, CY LEGAULT, R NADAL, C CHERIET, M LAM, L Centre for Pattern Recognition & Machine Intelligence Concordia University Montr&eacute al Canada H3G 1M8

This paper gives an assessment of the current state of the art in handwriting recognition. It summarizes the lessons learned, the difficulties involved, and the challenges ahead. Based on a review of the recent achievements in off-line computer recognition of totally unconstrained handwritten characters, and extensive research, the authors attempt to identify new frontiers for research which may lead to further breakthroughs in this field. They will present some evidences and novel ideas on ways of stretching the limits of handwriting recognition systems aiming at outperforming human beings.

关键词： HANDWRITING recognition FEATURE EXTRACTION IMPROVING HANDWRITING RECOGNIZERS recognition BY EXPERTS

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Isolated handwritten Farsi numerals recognition using sparse and over-complete representations

Isolated handwritten Farsi numerals recognition using sparse...

引用

ICDAR2009 - 10th International Conference on Document Analysis and recognition

作者： Pan, W.M. Bui, T.D. Suen, C.Y. Center for Pattern Recognition and Machine Intelligence Concordia University Canada

ISBN: (纸本)9780769537252

A new isolated handwritten Farsi numeral recognition algorithm is proposed in this paper, which exploits the sparse and over-complete structure from the handwritten Farsi numeral data. In this research, the sparse structure is represented as an over-complete dictionary, which is learned by the K-SVD algorithm. These atoms in this dictionary are adopted to initialize the first layer of the Convolutional Neural Network (CNN), the latter is then trained to do the classification task. Data distortion techniques are also applied to promote the generalization capability of the trained classifier. Experiments have shown that good results have been achieved in CENPARMI handwritten Farsi numeral database. © 2009 IEEE.

关键词： Neural networks

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Deep Learning on Binary patterns for Face recognition

Deep Learning on Binary Patterns for Face Recognition

引用

2018 International Conference on Computational intelligence and Data Science, ICCIDS 2018

作者： Vinay, A. Gupta, Abhijay Bharadwaj, Aprameya Srinivasan, Arvind Murthy, K.N. Balasubramanya Natarajan, S. Center for Pattern Recognition and Machine Intelligence PES University Bengaluru India

In this paper an efficient and robust method for real-time face recognition is proposed. As a part of pre-processing to remove noise and unwanted features, a filter is applied to the images of standard datasets. Subsequently binary patterns are extracted from these images which are further fed into multilayer perceptron to classify images. The proposed method is tested on four benchmark datasets namely FACES94, FACES95, FACES96 and Grimace which pose challenges in illumination, pose, expression, head scale and rotation. This method delivers accuracies in the neighbourhood of 91% when tested on these datasets. Our methodology was further extended to embedded systems like Raspberry Pi 3 to give it more real-time and practical scenario. This test gave promising results and proved the model to be efficient and useful in day to day scenarios for face recognition. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Neural networks

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Enhancement of Degraded CCTV Footage for Forensic Analysis 3rd

Enhancement of Degraded CCTV Footage for Forensic Analysis

引用

3rd International Conference on Innovative Computing and Communication, ICICC 2020

作者： Vinay, A. Lokesh, Aditya Kamath, Vinayaka R. Murty, K.N.B. Natarajan, S. Center for Pattern Recognition and Machine Intelligence PES University Bengaluru India

ISBN: (纸本)9789811551123

Forensic analysis has proved to be one of the most utilitarian tool in investigating crime. Forensic analysis provides evidence/basic information of the said crime through analysis of physical evidence. In this paper, we present a scintillating technique to enhance the image quality of CCTV video to assist in the investigation of criminal cases. This paper, address the issue of lack of a budget surplus for a high-quality image/video capturing device. The problem of enhancing images is addressed by pure image processing methods and machine learning techniques. The paper analyzes both techniques and further concludes that the machine learning approach produces a more efficient result. The applications of this technique can range from simple image filtering/beautification to forensic image processing. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Crime

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