Poster Abstract: A Novel and Efficient Approach to Evaluate Biometric Features for User Identification

Classifications based on biometric features are widely used in modern healthcare applications, including user identification, authentication, and tracking. The complexity and accuracy of classification algorithms largely depend on the size and the quality of the feature set used to build classifiers. Feature evaluation and selection are critical steps to decide a small set of high-quality features to build accurate and efficient classifiers. This paper proposes a novel and efficient approach to evaluate and select biometric features for user identification applications based on activity sensor data collected from the user's wrists. For each feature, we first generate an NRMSD matrix, each entry of which represents the similarity level of any two users. Then, we define a heuristic, the Farness value to evaluate the quality of the feature based on the NRMSD matrix of the feature. Finally, we select a set of high-quality features whose Farness value is higher than 0.10.