Predictive biometrics: a review and analysis of predicting personal characteristics from biometric data

Abstract

Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, 'higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future.

Password policy characteristics and keystroke biometric authentication

“…Our study can help inform their experimental design. Of our key findings, it is interesting to have identified that the capabilities of a key dynamic system are at their best with no substitution and the password is shorter in length (6). This means that implementing a keystroke biometric system, alongside a strong password policy, might have a negative impact on the biometric system.…”

“…Biometric systems provide an alternative approach to authentication whereby physiological, behavioural and chemical characteristics are sensed and used to authenticate a user in replacement (or in addition) to a password. Biometric systems have always been heavily researched, and there is a wealth of literature on the different types and their applications, as presented by these extensive surveys [5,6]. For example, in terms of physiological, fingerprints, and facial features are examples.…”

The Transition From Intelligent to Affective Tutoring System: A Review and Open Issues

“…In the literature, physiological emotion recognition also plays an important role in emotion recognition, which can be inferred from different ways such as biometric [132], [133], EEG signal measurements [134], [135]. As discussed above, these approaches also have the same approaches, such as data collection, pre-processing, and computational intelligence methods, to infer emotions.…”

A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States

“…Studies of the applications of affective biometrics can be found in the literature in the fields of brain biometric analysis [ 15 ], predictive biometrics [ 16 ], keystroke dynamics [ 17 ], applications in education [ 18 ], consumer neuroscience [ 19 ], adaptive biometric systems [ 20 ], emotion recognition from gait analyses [ 21 ], ECG databases [ 22 ], and others. Several works on affective states have integrated multiple biometric and neuroscience methods, but none have included an integrated review of the application of neuroscience and biometrics and an analysis of all of the emotions and affective attitudes in Plutchik’s wheel of emotions.…”