论文标题
使用小数据集的自适应模板增强功能改进的人识别
Adaptive Template Enhancement for Improved Person Recognition using Small Datasets
论文作者
论文摘要
本文提出和评估了一种基于实例的脑电图分类（EEG）信号分类的方法。脑电图信号的非平稳性，再加上有限的训练数据以及潜在的嘈杂信号获取条件的苛刻模式识别任务，激发了本研究中报道的工作。所提出的自适应模板增强机制通过分别处理每个特征维度来改变特征级别的实例，从而改善类别的分离和更好的查询类匹配。在许多情况下，将提出的新的基于实例的学习算法与一些相关算法进行了比较。使用单个干燥传感器使用低成本系统获得的临床64级电极EEG数据库以及使用低成本系统获得的低质量（高噪声水平）EEG数据库进行了生物识别人识别的评估。所提出的方法在识别和验证方案中都显着提高了分类精度。特别是，这种新方法可为嘈杂的脑电图数据提供良好的分类性能，表明其潜在的适用于广泛的应用程序。
A novel instance-based method for the classification of electroencephalography (EEG) signals is presented and evaluated in this paper. The non-stationary nature of the EEG signals, coupled with the demanding task of pattern recognition with limited training data as well as the potentially noisy signal acquisition conditions, have motivated the work reported in this study. The proposed adaptive template enhancement mechanism transforms the feature-level instances by treating each feature dimension separately, hence resulting in improved class separation and better query-class matching. The proposed new instance-based learning algorithm is compared with a few related algorithms in a number of scenarios. A clinical grade 64-electrode EEG database, as well as a low-quality (high-noise level) EEG database obtained with a low-cost system using a single dry sensor have been used for evaluations in biometric person recognition. The proposed approach demonstrates significantly improved classification accuracy in both identification and verification scenarios. In particular, this new method is seen to provide a good classification performance for noisy EEG data, indicating its potential suitability for a wide range of applications.
