Cancelable HD-SEMG Biometric Identification via Deep Feature Learning
Cancelable HD-SEMG Biometric Identification via Deep Feature Learning
复制标题
通过深度特征学习可取消 HD-SEMG 生物特征识别
DOI：
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复制
发表时间：
2021
影响因子：
7.7
通讯作者：
Wei Chen
中科院分区：
文献类型：
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作者：
Jiahao Fan;Xinyu Jiang;Xiangyu Liu;Xian Zhao;Xinming Ye;C. Dai;M. Akay;Wei Chen
传统的生物识别模式，如面部、指纹和虹膜，容易受到模仿和规避。因此，个人识别需要具有可取消属性的安全生物识别模式，特别是在智能医疗保健应用中。在这里，我们开发了一个使用高密度表面肌电图（HD-sEMG）作为生物特征的人识别模型。在该模型中，HD-sEMG生物特征模板是可取消的，用户可以通过手指的等长收缩来定制。使用卷积神经网络（cnn）实现的深度特征学习方法从HD-sEMG信号中捕获用户特定模式并做出识别决策。该模型已在22个受试者上进行了验证，训练和测试数据来自两个不同的日子。44个身份（22个受试者× 2个账户）的rank-1识别正确率和等错误率分别达到87.23%和4.66%。该模型的跨日识别精度高于文献中报道的先前方法的结果。本文还对该模型的可用性和效率进行了研究，表明了该模型在实际应用中的潜力。
Conventional biometric modalities, such as the face, fingerprint, and iris, are vulnerable against imitation and circumvention. Accordingly, secure biometric modalities with cancelable properties are needed for personal identification, especially in smart healthcare applications. Here we developed a person identification model using high-density surface electromyography (HD-sEMG) as biometric traits. In this model, the HD-sEMG biometric templates are cancelable and could be customized by the users through finger isometric contractions. A deep feature learning approach, implemented by convolutional neural networks (CNNs) is used to capture user-specific patterns from HD-sEMG signals and make identification decisions. This model has been validated on twenty-two subjects, with training and testing data acquired from two different days. The rank-1 identification accuracy and equal error rate for 44 identities (22 subjects × 2 accounts) can reach 87.23% and 4.66%, respectively. The cross-day identification accuracy of the proposed model is higher than the results of previous methods reported in the literature. The usability and efficiency of the proposed model are also investigated, indicating its potentials for practical applications.