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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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10833644/ |
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| Summary: | 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. |
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| ISSN: | 2169-3536 |