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Poor security practices among smartphone users, such as the use of simple, easily guessed passcodes for logins, are a result of the effort required to memorize stronger ones. In this paper, we devise a concept of “open code” biometric tap pad to authenticate smartphone users, which eliminates the need of memorizing secret codes. A biometric tap pad consists of a grid of buttons each labeled with a unique digit. The user attempting to log into the phone will tap these buttons in a given sequence. He/she will not memorize this tap sequence. Instead, the sequence will be displayed on the screen. The focus here is how the user types the sequence. This typing behavior is used for authentication. An open code biometric tap pad has several advantages, such as (1) users do not need to memorize passcodes, (2) manufacturers do not need to include extra sensors, and (3) onlookers have no chance to practice shoulder-surfing. We designed three tap pads and incorporated them into an Android app. We evaluated the performance of these tap pads by experimenting with three sequence styles and five different fingers: two thumbs, two index fingers, and the “usual” finger. We collected data from 33 participants over two weeks. We tested three machine learning algorithms: Support Vector Machine, Artificial Neural Network, and Random Forest. Experimental results show significant promise of open code biometric tap pads as a solution to the problem of weak smartphone security practices used by a large segment of the population.
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We investigate the viability of the capacitive swipe gesture as a biometric modality. While the regular swipe gesture and the capacitive image have been widely explored in biometric literature, the capacitive swipe gesture is fairly new in this line of research. To our knowledge, only one recent study has explored the capacitive swipe gesture, and demonstrated its promise. However, that study is limited by a number of factors, such as using a very small data set in the experiments, collecting data in a single session, allowing the same impostor in both training and testing phases of authentication models, etc. In our paper, we address all these limitations, and rigorously explore the capacitive swipe gesture by creating a new large data set. Additionally, we develop a new technique to preprocess capacitive swipe gesture data, and demonstrate its effectiveness by comparing with existing techniques. A large set of experiments with four machine learning classifiers and two swipe directions prove that the capacitive swipe gesture can be effectively used for user authentication in smartphones.
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In this paper, we provide a consistent, inexpensive, and easy to use graphical user interface (GUI) smart phone application named Sleep Apnea Screener (SAS) that can diagnosis Obstructive Sleep Apnea (OSA) based on demographic data such as: gender, age, height, BMI, neck circumference, waist, etc., allowing a tentative diagnosis of OSA without the need for overnight tests. The developed smart phone application can diagnosis sleep apnea using a model trained with 620 samples collected from a sleep center in Corpus Christi, TX. Two machine learning classifiers (i.e., Logistic Regression (LR) and Support Vector Machine (SVM)) were used to diagnosis OSA. Our preliminary results show that at-home OSA screening is indeed possible, and that our application is effective method for covering large numbers of undiagnosed cases.
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- Conference Paper (1)
- Journal Article (2)
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- English (2)