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Image creation and retention are growing at an exponential rate. Individuals produce more images today than ever in history and often these images contain family. In this paper, we develop a framework to detect or identify family in a face image dataset. The ability to identify family in a dataset of images could have a critical impact on finding lost and vulnerable children, identifying terror suspects, social media interactions, and other practical applications. We evaluated our framework by performing experiments on two facial image datasets, the Y-Face and KinFaceW, comprising 37 and 920 images, respectively. We tested two feature extraction techniques, namely PCA and HOG, and three machine learning algorithms, namely K-Means, agglomerative hierarchical clustering, and K nearest neighbors. We achieved promising results with a maximum detection rate of 94.59% using K-Means, 89.18% with agglomerative clustering, and 77.42% using K-nearest neighbors. © 2020 World Scientific Publishing Company.
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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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Weaknesses in smartphone security pose a severe privacy threat to users. Currently, smartphones are secured through methods such as passwords, fingerprint scanners, and facial recognition cameras. To explore new methods and strengthen smartphone security, we developed a capacitive swipe based user authentication and identification technique. Swipe is a gesture that a user performs throughout the usage of a smartphone. Our methodology focuses on using the capacitive touchscreen to capture the user's swipe. While the user swipes, a series of capacitive frames are captured for each swipe. We developed an algorithm to process this series of capacitive frames pertaining to the swipe. While different swipes may contain different numbers of capacitive frames, our algorithm normalizes the frames by constructing the same number of frames for every swipe. After applying the algorithm, we transform the normalized frames into gray scale images. We apply principal component analysis (PCA) to these images to extract principal components, which are then used as features to authenticate/identify the user. We tested random forest (RF) and support vector machine (SVM) algorithms as classifiers. For authentication, the performance of SVM (tested with left swipes) was more promising than RF, yielding a maximum accuracy of 79.88% with an FAR and FRR of 15.84% and 50%, respectively. SVM (tested with right swipes) produced our maximum identification accuracy at 57.81% along with an FAR and FRR of 0.60% and 42.18%, respectively. © 2020 IEEE.
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