计算机科学 ›› 2020, Vol. 47 ›› Issue (11): 25-31.doi: 10.11896/jsjkx.200200044
所属专题： 智能移动身份认证
周志一1, 宋冰2, 段鹏松1, 曹仰杰1
ZHOU Zhi-yi1, SHONG Bing2, DUAN Peng-song1, CAO Yang-jie1
摘要： 身份识别作为普适计算和人机交互领域的重要研究内容,受到研究者的广泛关注。基于WiFi信号的传统身份识别方法虽然取得了较大的进展,但仍然面临分类能力弱、模型存储代价高、训练时间长等问题。对此,提出了基于多层神经网络的轻量级步态识别模型(Light Weight Identification,LWID)。该方法首先通过将原始时序数据进行图片化重构,最大限度地保留了不同载波间的特征信息;然后通过设计一种仿生的Balloon机制,实现了对网络层中神经元数量的裁剪,并通过联合使用不同尺寸的卷积核,实现了对数据中特征的提取与特征图中通道信息的整合,从而在提高模型分类能力的前提下实现了模型规模的轻量化。实验结果表明,所提模型在50人的数据集中取得了98.8％的识别率。与传统的基于WiFi信号的身份识别模型相比,所提模型具有更强的分类能力与鲁棒性,同时该模型可以压缩至现有同等精度图片识别模型大小的6.14%。
中图分类号:
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