Investigating gender recognition in smartphones using accelerometer and gyroscope sensor readings

摘要

This paper presents an approach for gender recognition using behavioral biometrics in smartphones. Specifically, this work investigates gender recognition using gait data acquired from the inbuilt accelerometer and gyroscope sensors of a smartphone. The proposed approach involves computation of curvature of the gait signals. In order to capture the local variations of estimated curvatures, we employed histogram features of multi-level local pattern (MLP) and local binary pattern (LBP). In this work, support vector machine (SVM) and aggregate bootstrapping (bagging) classifiers are employed for identification of gender based on the extracted features. Performance evaluation of the proposed approach on a database of 252 gait data collected from 42 subjects yielded promising results. Our experimental results also show that MLP performs better than LBP for feature extraction, while bagging outperforms SVM for classification.