Gender Detection Based on Gait Data: A Deep Learning Approach With Synthetic Data Generation and Continuous Wavelet Transform

Smart devices equipped with various sensors enable the acquisition of users' behavioral biometrics. These sensor data capture variations in users' interactions with the devices, which can be analyzed to extract valuable information such as user activity, age group, and gender. In this study, we investigate the feasibility of using gait data for gender detection of users. To achieve this, we propose a novel gender detection scheme based on a deep learning approach, incorporating synthetic data generation and continuous wavelet transform (CWT). In this scheme, the real dataset is first divided into training and test datasets, and then synthetic data are intelligently generated using various techniques to augment the existing training data. Subsequently, CWT is used as the feature extraction module, and its outputs are fed into a deep learning model to detect the gender of users. Different deep learning models, including convolutional neural network (CNN) and long short-term memory (LSTM), are employed in classification. Consequently, we evaluate our proposed framework on different publicly available datasets. On the BOUN Sensor dataset, we obtain an accuracy of 94.83%, marking a substantial 6.5% enhancement over the prior highest rate of 88.33%. Additionally, we achieve 86.27% and 88.15% accuracy on the OU-ISIR Android and OU-ISIR Center IMUZ datasets, respectively. Our experimental results demonstrate that our proposed model achieves high detection rates and outperforms previous methods across all datasets.