Journal of Clinical, Medical, and Diagnostic Research(JCMDR)

School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, Anhui, China

Publications

• Research Article   
  Towards Energy-Effective Multimodal Biometric Recognition Via Information Bottleneck Fusion Spiking Neural Networks
  Author(s): Yan Shen, Xiaoxu Yang*, Xu Liu, Jiashan Wan and Na Xia
  
  With the development of multimodal biometric recognition technology, addressing substantial differences in data type, scale, reso- lution, and quality among biometric modalities has become one of the key challenges in ensuring enhanced security and accuracy. However, most existing techniques fail to address modality imbalance caused by disparities across modalities, leading to over-re- liance on a single modality, degraded performance, and increased security vulnerabilities. Additionally, deploying traditional neural networks with full-precision floating-point representations on embedded devices is expensive and resource-intensive, fur- ther exacerbating security risks during end-to-end transmission. A new spiking neural networks multimodal biometric recognition model which incorporates two novel multimodal fusion methods is proposed. First, a spiking multimodal information bottleneck.. Read More»
  

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