论文标题
基于加速度计的步态识别的功能学习
Feature Learning for Accelerometer based Gait Recognition
论文作者
论文摘要
模式匹配的最新进展，例如语音或对象识别，支持特征学习的可行性，并通过深度学习解决方案进行步态识别。过去的论文评估了以监督方式培训的深层神经网络。在这项工作中，我们研究了受监督和无监督的方法。使用将类似体系结构纳入端到端模型和自动编码器的功能提取器，根据其学习步态验证系统的良好表示能力进行比较。在IDNET数据集上训练了两个特征提取器，然后在Zju-Gaitaccel数据集上用于特征提取。结果表明，关于其功能学习能力，自动编码器非常接近判别端到端模型，并且无论培训策略如何，完全卷积模型都能学习良好的功能表示。
Recent advances in pattern matching, such as speech or object recognition support the viability of feature learning with deep learning solutions for gait recognition. Past papers have evaluated deep neural networks trained in a supervised manner for this task. In this work, we investigated both supervised and unsupervised approaches. Feature extractors using similar architectures incorporated into end-to-end models and autoencoders were compared based on their ability of learning good representations for a gait verification system. Both feature extractors were trained on the IDNet dataset then used for feature extraction on the ZJU-GaitAccel dataset. Results show that autoencoders are very close to discriminative end-to-end models with regards to their feature learning ability and that fully convolutional models are able to learn good feature representations, regardless of the training strategy.
