Extrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds

Sparsity-based representations have recently led to notable results in various visual recognition tasks. In a separate line of research, Riemannian manifolds have been shown useful for dealing with features and models that do not lie in Euclidean spaces. With the aim of building a bridge between the two realms, we address the problem of sparse coding and dictionary learning over the space of linear subspaces, which form Riemannian structures known as Grassmann manifolds. To this end, we propose to embed Grassmann manifolds into the space of symmetric matrices by an isometric mapping. This in turn enables us to extend two sparse coding schemes to Grassmann manifolds. Furthermore, we propose closed-form solutions for learning a Grassmann dictionary, atom by atom. Lastly, to handle non-linearity in data, we extend the proposed Grassmann sparse coding and dictionary learning algorithms through embedding into Hilbert spaces. Experiments on several classification tasks (gender recognition, gesture classification, scene analysis, face recognition, action recognition and dynamic texture classification) show that the proposed approaches achieve considerable improvements in discrimination accuracy, in comparison to state-of-the-art methods such as kernelized Affine Hull Method and graph-embedding Grassmann discriminant analysis.Comment: Appearing in International Journal of Computer Visio

Mobile Quantification and Therapy Course Tracking for Gait Rehabilitation

This paper presents a novel autonomous quality metric to quantify the rehabilitations progress of subjects with knee/hip operations. The presented method supports digital analysis of human gait patterns using smartphones. The algorithm related to the autonomous metric utilizes calibrated acceleration, gyroscope and magnetometer signals from seven Inertial Measurement Unit attached on the lower body in order to classify and generate the grading system values. The developed Android application connects the seven Inertial Measurement Units via Bluetooth and performs the data acquisition and processing in real-time. In total nine features per acceleration direction and lower body joint angle are calculated and extracted in real-time to achieve a fast feedback to the user. We compare the classification accuracy and quantification capabilities of Linear Discriminant Analysis, Principal Component Analysis and Naive Bayes algorithms. The presented system is able to classify patients and control subjects with an accuracy of up to 100\%. The outcomes can be saved on the device or transmitted to treating physicians for later control of the subject's improvements and the efficiency of physiotherapy treatments in motor rehabilitation. The proposed autonomous quality metric solution bears great potential to be used and deployed to support digital healthcare and therapy.Comment: 5 Page

HUMAN GENDER CLASSIFICATION USING KINECT SENSOR: A REVIEW

Human Gender Classification using Kinect sensor aims to classifying people’s gender based on their outward appearance. Application areas of Kinect sensor technology includes security, marketing, healthcare, and gaming. However, because of the changes in pose, attire, and illumination, gender determination with the Kinect sensor is not a trivial task. It is based on a variety of characteristics, including biological, social network, face, and body aspects. In recent years, gender classification that utilizes the Kinect sensor became a popular and essential way for accurate gender classification. A variety of methods and approaches, like machine learning, convolutional neural networks, sport vector machine (SVM), etc., have been used for gender classification using a Kinect sensor. This paper presents the state of the art for gender classification, with a focus on the features, databases, procedures, and algorithms used in it. A review of recent studies on this subject using the Kinect sensor and other technologies is provided, together with information on the variables that affect the classification\u27s accuracy. In addition, several publicly accessible databases or datasets are used by researchers to classify people by gender are covered. Finlay, this overview offers insightful information about the potential future avenues for research on Kinect-based human gender classification