New security systems, methods or techniques need to have their performance evaluated in conditions that closely resemble a real-life situation. The effectiveness with which individual identity can be predicted in different scenarios can benefit from seeking a broad base of identity evidence. Many approaches to the implementation of biometric-based identification systems are possible, and different configurations are likely to generate significantly different operational characteristics. The choice of implementational structure is, therefore, very dependent on the performance criteria, which is most important in any particular task scenario. The issue of improving performance can be addressed in many ways, but system configurations based on integrating different information sources are widely adopted in order to achieve this. Thus, understanding how each data information can influence performance is very important. The use of similar modalities may imply that we can use the same features. However, there is no indication that very similar (such as keyboard and touch keystroke dynamics, for example) basic biometrics will perform well using the same set of features. In this paper, we will evaluate the merits of using a three-modal hand-based biometric database for user prediction focusing on feature selection as the main investigation point. To the best of our knowledge, this is the first thought-out analysis of a database with three modalities that were collected from the same users, containing keyboard keystroke, touch keystroke and handwritten signature. First, we will investigate how the keystroke modalities perform, and then, we will add the signature in order to understand if there is any improvement in the results. We have used a wide range of techniques for feature selection that includes filters and wrappers (genetic algorithms), and we have validated our findings using a clustering technique.

中文翻译：

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巴西三模态基于手的行为生物识别的可预测性调查：特征选择和特征融合方法

新的安全系统、方法或技术需要在与现实情况非常相似的条件下对其性能进行评估。在不同场景中预测个人身份的有效性可以从广泛的身份证据基础中受益。许多实现基于生物特征的识别系统的方法都是可能的，不同的配置可能会产生显着不同的操作特性。因此，实现结构的选择非常依赖于性能标准，这在任何特定任务场景中都是最重要的。提高性能的问题可以通过多种方式解决，但广泛采用基于集成不同信息源的系统配置来实现这一目标。因此，了解每个数据信息如何影响性能非常重要。使用相似的模态可能意味着我们可以使用相同的功能。然而，没有迹象表明非常相似（例如键盘和触摸键击动态）的基本生物识别技术在使用相同的功能集时会表现良好。在本文中，我们将评估使用三模态基于手的生物特征数据库进行用户预测的优点，重点是特征选择作为主要研究点。据我们所知，这是对从同一用户收集的三种模态的数据库进行的首次深思熟虑的分析，包括键盘击键、触摸击键和手写签名。首先，我们将研究击键方式的执行方式，然后，我们将添加签名以了解结果是否有任何改进。我们使用了广泛的特征选择技术，包括过滤器和包装器（遗传算法），并且我们使用聚类技术验证了我们的发现。