{"title":"Compact convolution transformer with cross-feature aggregation for hand-gesture recognition","authors":"Satya Narayan , Praful Hambarde , Santosh Kumar Vipparthi , Arka Prokash Mazumdar , Subrahmanyam Murala","doi":"10.1016/j.compeleceng.2025.110727","DOIUrl":null,"url":null,"abstract":"<div><div>Hand Gesture Recognition (HGR) plays a crucial role in intuitive human–computer interaction but continues to face challenges such as complex backgrounds, lighting variations, occlusions, and limited training data. To overcome these issues, we propose a Cross Feature Aggregation Compact Convolution Transformer (CrFe-CCT) that integrates multiscale convolutional features with a lightweight transformer architecture. In the proposed CrFe-CCT network, includes the multi-scale Cross Feature Aggregation (CrFe) and CCT modules. The CrFe module help to enhances feature robustness by fusing contextual information across scales, leading to improved recognition accuracy while maintaining low computational complexity. Also, CCT module help to preserve local spatial relationships. Unlike conventional transformers that rely on large-scale data, CrFe-CCT enables efficient learning on both small and large datasets. The experimental results demonstrate that the proposed CrFe-CCT outperforms existing state-of-the-art approaches on subject-dependent datasets, achieving accuracies of 91.95%(HGR-1), 97.70% (MUGD Set1), 95.50% (MUGD Set2), 99.06%(MUGD Set3), 99.82% (NUS-II), 99.90% (ASL-Finger Spelling (FS), and 96.80% (OUHands). On subject-independent datasets, the CrFe-CCT network achieves 40.43%(HGR-1), 85.11% (MUGD), 70.34 (NUS-II Dataset), 82.20% (ASL-Finger Spelling (FS)), respectively. Furthermore, it demonstrates superior efficiency with parameters, memory usage, FLOPs, inference time, and a throughput of images for real-world HGR applications.</div><div>The source code is available at <span><span>https://github.com/satyantazi/CrFe-CCT</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"128 ","pages":"Article 110727"},"PeriodicalIF":4.9000,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Electrical Engineering","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0045790625006706","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0
Abstract
Hand Gesture Recognition (HGR) plays a crucial role in intuitive human–computer interaction but continues to face challenges such as complex backgrounds, lighting variations, occlusions, and limited training data. To overcome these issues, we propose a Cross Feature Aggregation Compact Convolution Transformer (CrFe-CCT) that integrates multiscale convolutional features with a lightweight transformer architecture. In the proposed CrFe-CCT network, includes the multi-scale Cross Feature Aggregation (CrFe) and CCT modules. The CrFe module help to enhances feature robustness by fusing contextual information across scales, leading to improved recognition accuracy while maintaining low computational complexity. Also, CCT module help to preserve local spatial relationships. Unlike conventional transformers that rely on large-scale data, CrFe-CCT enables efficient learning on both small and large datasets. The experimental results demonstrate that the proposed CrFe-CCT outperforms existing state-of-the-art approaches on subject-dependent datasets, achieving accuracies of 91.95%(HGR-1), 97.70% (MUGD Set1), 95.50% (MUGD Set2), 99.06%(MUGD Set3), 99.82% (NUS-II), 99.90% (ASL-Finger Spelling (FS), and 96.80% (OUHands). On subject-independent datasets, the CrFe-CCT network achieves 40.43%(HGR-1), 85.11% (MUGD), 70.34 (NUS-II Dataset), 82.20% (ASL-Finger Spelling (FS)), respectively. Furthermore, it demonstrates superior efficiency with parameters, memory usage, FLOPs, inference time, and a throughput of images for real-world HGR applications.
The source code is available at https://github.com/satyantazi/CrFe-CCT.
期刊介绍:
The impact of computers has nowhere been more revolutionary than in electrical engineering. The design, analysis, and operation of electrical and electronic systems are now dominated by computers, a transformation that has been motivated by the natural ease of interface between computers and electrical systems, and the promise of spectacular improvements in speed and efficiency.
Published since 1973, Computers & Electrical Engineering provides rapid publication of topical research into the integration of computer technology and computational techniques with electrical and electronic systems. The journal publishes papers featuring novel implementations of computers and computational techniques in areas like signal and image processing, high-performance computing, parallel processing, and communications. Special attention will be paid to papers describing innovative architectures, algorithms, and software tools.