Biometric techniques are often used as an extra security factor in authenticating human users. Numerous biometrics have been proposed and evaluated, each with its own set of benefits and pitfalls. Static biometrics (such as fingerprints) are geared for discrete operation, to identify users, which typically involves some user burden. Meanwhile, behavioral biometrics (such as keystroke dynamics) are well suited for continuous, and sometimes more unobtrusive, operation. One important application domain for biometrics is deauthentication, a means of quickly detecting absence of a previously authenticated user and immediately terminating that user's active secure sessions. Deauthentication is crucial for mitigating so called Lunchtime Attacks, whereby an insider adversary takes over (before any inactivity timeout kicks in) authenticated state of a careless user who walks away from her computer. Motivated primarily by the need for an unobtrusive and continuous biometric to support effective deauthentication, we introduce PoPa, a new hybrid biometric based on a human user's seated posture pattern. PoPa captures a unique combination of physiological and behavioral traits. We describe a low cost fully functioning prototype that involves an office chair instrumented with 16 tiny pressure sensors. We also explore (via user experiments) how PoPa can be used in a typical workplace to provide continuous authentication (and deauthentication) of users. We experimentally assess viability of PoPa in terms of uniqueness by collecting and evaluating posture patterns of a cohort of users. Results show that PoPa exhibits very low false positive, and even lower false negative, rates. In particular, users can be identified with, on average, 91.0 accuracy. Finally, we compare pros and cons of PoPa with those of several prominent biometric based deauthentication techniques.
Finally, @cite_2 investigated how to use fewer sensors to detect posture. To determine optimal sensor placement, a classifier is constructed that learns the probabilistic model between the chosen subset of sensor values and feature vectors used for posture classification. With @math sensors, classification accuracy of 87 sensor placement strategy.
@cite_2
2160904077 1974477890 2104577112 2786918876
In this paper, we present a methodology for recognizing seatedpostures using data from pressure sensors installed on a chair.Information about seated postures could be used to help avoidadverse effects of sitting for long periods of time or to predictseated activities for a human-computer interface. Our system designdisplays accurate near-real-time classification performance on datafrom subjects on which the posture recognition system was nottrained by using a set of carefully designed, subject-invariantsignal features. By using a near-optimal sensor placement strategy,we keep the number of required sensors low thereby reducing costand computational complexity. We evaluated the performance of ourtechnology using a series of empirical methods including (1)cross-validation (classification accuracy of 87 for ten posturesusing data from 31 sensors), and (2) a physical deployment of oursystem (78 classification accuracy using data from 19sensors). In this paper we propose a novel method to detect and record various posture-based events of interest in a typical elderly monitoring application in a home surveillance scenario. These events include standing, sitting, bending squatting, side lying and lying toward the camera. The projection histograms of segmented human body silhouette are used as the main feature vector for posture classification. k-nearest neighbor (k-NN) algorithm and evidence accumulation technique is proposed to infer human postures. With this technique we have achieved a robust recognition rate of above 90 and a stable classifier's output. The modified classifier structure also improves greatly the recognition rate of lying toward the camera events as compared to the result of classifier's structure in GHOST (, 1998). Furthermore, we use the speed of fall to differentiate real fall incident and an event where the person is simply lying without falling. A cascade face detector uses a sequence of node classifiers to distinguish faces from nonfaces. This paper presents a new approach to design node classifiers in the cascade detector. Previous methods used machine learning algorithms that simultaneously select features and form ensemble classifiers. We argue that if these two parts are decoupled, we have the freedom to design a classifier that explicitly addresses the difficulties caused by the asymmetric learning goal. There are three contributions in this paper: The first is a categorization of asymmetries in the learning goal and why they make face detection hard. The second is the forward feature selection (FFS) algorithm and a fast precomputing strategy for AdaBoost. FFS and the fast AdaBoost can reduce the training time by approximately 100 and 50 times, in comparison to a naive implementation of the AdaBoost feature selection method. The last contribution is a linear asymmetric classifier (LAC), a classifier that explicitly handles the asymmetric learning goal as a well-defined constrained optimization problem. We demonstrated experimentally that LAC results in an improved ensemble classifier performance. The task of stance detection is to determine whether someone is in favor or against a certain topic. A person may express the same stance towards a topic using positive or negative words. In this paper, several features and classifiers are explored to find out the combination that yields the best performance for stance detection. Due to the large number of features, ReliefF feature selection method was used to reduce the large dimensional feature space and improve the generalization capabilities. Experimental analyses were performed on five datasets, and the obtained results revealed that a majority vote classifier of the three classifiers: Random Forest, linear SVM and Gaussian Naive Bayes classifiers can be adopted for stance detection task.