In this chapter, a novel technique to authenticate a mobile phone user irrespective of his/her typing position is presented. The user is never always in sitting position while using mobile phone. Thus, it becomes very important to check the accuracy of keystroke dynamics technique while taking input in all positions but authenticating the user irrespective of these positions. Three user positions were considered for input – sitting, walking, and relaxed. The input was taken in uncontrolled environment to get realistic results. Hold time, latency, and motion features using accelerometer data were extracted, and the analysis was done using random forest and KNN classifiers. The accelerometer data provides additional features like mean of all X, Y, and Z axis values. The inclusion of these features improved the results drastically and played a very significant role in determining the user typing behavior. An EER of 4.3% was achieved with a best FAR of 0.9% and an FRR of 15.2%.
