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Bimodal behavioral authentication framework based on decision fusion

The majority of proposed behavioral biometric systems on smartphones are unimodal based, which rely only on a single source of information such as gesture or keystrokes. Unfortunately, these systems are suffering from some problems such as noisy data and non-universality. Moreover, they provide lower authentication accuracy in compare with the physiological biometrics such as Face. To address these problems, we developed a bimodal authentication framework based on decision fusion. We conducted a field study by instrumenting the Android OS. We analyzed data from 52 participants during 30-day period. We present the prototype of our framework, where we developed two authentication modalities. First, a gesture authentication modality, which authenticate smartphone users based on touch gesture. Second, a keystrokes authentication modality, which authenticate smartphone users based on the way they type. We evaluated each authentication modality based on two schemes, classification scheme and anomaly detection scheme. Then we used the decision fusion method to enhance the accuracy of detection.