Face Detection Using Hybrid SNN-ANN to Process Neuromorphic Event Stream
This paper tackles the challenges of face detection, a vital computer vision task with wide-ranging applications, particularly in driver monitoring systems, where both accuracy and computational efficiency are crucial. Traditional frame-based methods often suffer from high computational complexity a...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10833644/ |
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| author | Waseem Shariff Paul Kielty Joe Lemley Peter Corcoran |
| author_facet | Waseem Shariff Paul Kielty Joe Lemley Peter Corcoran |
| author_sort | Waseem Shariff |
| collection | DOAJ |
| description | This paper tackles the challenges of face detection, a vital computer vision task with wide-ranging applications, particularly in driver monitoring systems, where both accuracy and computational efficiency are crucial. Traditional frame-based methods often suffer from high computational complexity and under-sampling issues. In turn this limits their effectiveness in real-time applications and increase energy budget. To overcome these limitations, this paper explores the benefits of neuromorphic event cameras, which capture asynchronous pixel-level changes, offering lower data processing demands and reduced latency. The paper proposes a hybrid architecture combining Spiking Neural Networks (SNNs) and Artificial Neural Networks (ANNs). This approach leverages the energy efficiency and low-latency of SNNs while maintaining the high accuracy of ANNs, resulting in a highly efficient and accurate face detection system. The proposed Spiking-Face approach demonstrates a 57.76% improvement in mean average precision (mAP) over state-of-the-art methods. Additionally, the paper provides a comprehensive analysis of the system’s performance across different temporal resolutions, showing that the system performs robustly and adapts effectively to varying conditions. This hybrid SNN-ANN architecture achieves up to three times higher computational efficiency compared to equivalent traditional ANN methods, significantly reducing computational complexity while maintaining accuracy. These findings underscore the potential of this hybrid architecture for real-time, energy-constrained applications. |
| format | Article |
| id | doaj-art-de8b689deee84275b864a76cdfc00b4f |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-de8b689deee84275b864a76cdfc00b4f2025-01-21T00:01:38ZengIEEEIEEE Access2169-35362025-01-01139844985610.1109/ACCESS.2025.352716010833644Face Detection Using Hybrid SNN-ANN to Process Neuromorphic Event StreamWaseem Shariff0https://orcid.org/0000-0001-7298-9389Paul Kielty1https://orcid.org/0000-0002-6259-1163Joe Lemley2https://orcid.org/0000-0002-0595-2313Peter Corcoran3https://orcid.org/0000-0003-1670-4793Department of Electronic and Electrical Engineering, College of Science and Engineering, C3I Laboratory, University of Galway, Galway, IrelandDepartment of Electronic and Electrical Engineering, College of Science and Engineering, C3I Laboratory, University of Galway, Galway, IrelandDrowsiness Team, FotoNation Ltd.-Tobii, Galway, IrelandDepartment of Electronic and Electrical Engineering, College of Science and Engineering, C3I Laboratory, University of Galway, Galway, IrelandThis paper tackles the challenges of face detection, a vital computer vision task with wide-ranging applications, particularly in driver monitoring systems, where both accuracy and computational efficiency are crucial. Traditional frame-based methods often suffer from high computational complexity and under-sampling issues. In turn this limits their effectiveness in real-time applications and increase energy budget. To overcome these limitations, this paper explores the benefits of neuromorphic event cameras, which capture asynchronous pixel-level changes, offering lower data processing demands and reduced latency. The paper proposes a hybrid architecture combining Spiking Neural Networks (SNNs) and Artificial Neural Networks (ANNs). This approach leverages the energy efficiency and low-latency of SNNs while maintaining the high accuracy of ANNs, resulting in a highly efficient and accurate face detection system. The proposed Spiking-Face approach demonstrates a 57.76% improvement in mean average precision (mAP) over state-of-the-art methods. Additionally, the paper provides a comprehensive analysis of the system’s performance across different temporal resolutions, showing that the system performs robustly and adapts effectively to varying conditions. This hybrid SNN-ANN architecture achieves up to three times higher computational efficiency compared to equivalent traditional ANN methods, significantly reducing computational complexity while maintaining accuracy. These findings underscore the potential of this hybrid architecture for real-time, energy-constrained applications.https://ieeexplore.ieee.org/document/10833644/Event-cameraface-detectionspiking neural networkhybrid SNN ANNcomputational efficiency |
| spellingShingle | Waseem Shariff Paul Kielty Joe Lemley Peter Corcoran Face Detection Using Hybrid SNN-ANN to Process Neuromorphic Event Stream IEEE Access Event-camera face-detection spiking neural network hybrid SNN ANN computational efficiency |
| title | Face Detection Using Hybrid SNN-ANN to Process Neuromorphic Event Stream |
| title_full | Face Detection Using Hybrid SNN-ANN to Process Neuromorphic Event Stream |
| title_fullStr | Face Detection Using Hybrid SNN-ANN to Process Neuromorphic Event Stream |
| title_full_unstemmed | Face Detection Using Hybrid SNN-ANN to Process Neuromorphic Event Stream |
| title_short | Face Detection Using Hybrid SNN-ANN to Process Neuromorphic Event Stream |
| title_sort | face detection using hybrid snn ann to process neuromorphic event stream |
| topic | Event-camera face-detection spiking neural network hybrid SNN ANN computational efficiency |
| url | https://ieeexplore.ieee.org/document/10833644/ |
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