Behavioral Biometrics

Behavioral biometrics leverages an individual's unique behavioral patterns, such as typing rhythm, mouse movements, or in-game gestures, for authentication. Current research focuses on improving accuracy and robustness using various machine learning models, including Support Vector Machines, Random Forests, Recurrent Neural Networks (like LSTMs), and deep learning architectures such as DenseNets and autoencoders, often incorporating multimodal data fusion techniques. This field is significant for enhancing security in various applications, from continuous authentication on mobile devices and VR systems to improving the efficiency of user enrollment processes.