Browsing by Subject "Euclidean"
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Item Open Access Investigation of personal variations in activity recognition using miniature inertial sensors and magnetometers(IEEE, 2012-04) Yurtman, Aras; Barshan, BillurIn this paper, data acquired from five sensory units mounted on the human body, each containing a tri-axial accelerometer, gyroscope, and magnetometer, during 19 different human activities is used to calculate inter-subject and inter-activity variations using different methods and the results are summarized in various forms. Absolute, Euclidean, and dynamic time-warping distances are used to assess the similarity of the signals. The comparisons are made using the raw and normalized time-domain data, raw and normalized feature vectors. Firstly, inter-subject distances are averaged out per activity and per subject. Based on these values, the "best" subject is defined and identified according to his/her average distance to the others. Then, the averages and standard deviations of inter-activity distances are presented per subject, per unit, and per sensor. Moreover, the effects of removing the mean and the different distance measures on the results are discussed. © 2012 IEEE.Item Open Access Rule based segmentation and subject identification using fiducial features and subspace projection methods(Academy Publisher, 2007) Ince, E. A.; Ali, S. A.This paper describes a framework for carrying out face recognition on a subset of standard color FERET database using two different subspace projection methods, namely PCA and Fisherfaces. At first, a rule based skin region segmentation algorithm is discussed and then details about eye localization and geometric normalization are given. The work achieves scale and rotation invariance by fixing the inter ocular distance to a selected value and by setting the direction of the eye-to-eye axis. Furthermore, the work also tries to avoid the small sample space (S3) problem by increasing the number of shots per subject through the use of one duplicate set per subject. Finally, performance analysis for the normalized global faces, the individual extracted features and for a multiple component combination are provided using a nearest neighbour classifier with Euclidean and/or Cosine distance metrics.