  Using physical interactive devices like mouse and keyboards hinders
naturalistic human-machine interaction and increases the probability of surface
contact during a pandemic. Existing gesture-recognition systems do not possess
user authentication, making them unreliable. Static gestures in current
gesture-recognition technology introduce long adaptation periods and reduce
user compatibility. Our technology places a strong emphasis on user recognition
and safety. We use meaningful and relevant gestures for task operation,
resulting in a better user experience. This paper aims to design a robust,
face-verification-enabled gesture recognition system that utilizes a graphical
user interface and primarily focuses on security through user recognition and
authorization. The face model uses MTCNN and FaceNet to verify the user, and
our LSTM-CNN architecture for gesture recognition, achieving an accuracy of 95%
with five classes of gestures. The prototype developed through our research has
successfully executed context-dependent tasks like save, print, control
video-player operations and exit, and context-free operating system tasks like
sleep, shut-down, and unlock intuitively. Our application and dataset are
available as open source.
