Web user behavioural recognition is the process by which web users are identified and distinguished through behavioural features. In this work, two sources of behavioural biometric data are analyzed for the development of this web user identification model, touch dynamics and the characteristics extracted from the periocular area related to the pupils, blinks and fixations. The approach adopted used to improve the overall performance of the multimodal biometric recognition system is based on a fusion at the Feature level to which different distance measure techniques (Euclidean, Bray-Curtis, Manhattan, Canberra, Chebyshev, Cosine) are applied to determine if the test sample belongs to the target subject. To further improve the system performance, we have applied multi-data processing methods such as Canonical Correlation Analysis (CCA) and Principal Component Analysis (PCA). The results obtained demonstrate the promise of these two different biometric traits and, above all, of their fusion. In fact, the fusion approach allows obtaining an accuracy higher than that of individual biometrics, reaching an accuracy of over 92%.

网络用户行为识别是通过行为特征识别和区分网络用户的过程。在这项工作中，分析了行为生物特征数据的两个来源，以开发此网络用户识别模型、触摸动态以及从与瞳孔、眨眼和注视相关的眼周区域提取的特征。用于提高多模态生物识别系统整体性能的方法基于特征级别的融合，不同的距离测量技术（欧几里得、布雷-柯蒂斯、曼哈顿、堪培拉、切比雪夫、余弦）被应用于确定是否测试样本属于目标对象。为了进一步提高系统性能，我们应用了典型相关分析（CCA）和主成分分析（PCA）等多数据处理方法。获得的结果证明了这两种不同的生物特征的前景，尤其是它们的融合。事实上，融合方法可以获得比个体生物识别更高的准确度，达到92%以上的准确度。