With the rapid development of personal computers and mobile devices, it is very important to properly authenticate a user's identity to protect the information and data stored on these devices. Due to various privacy and security concerns, contactless authentication has received much attention, among which in-air gesture based authentication is one promising solution. Motivated by this observation, in this work, we develop and implement a real-time in-air hand gesture-based user authentication system, where users can define or select various gestures and generate their credentials. Our system can verify a user using a deep learning-enabled inference framework without the need of being trained by a powerful device. Different from the state-of-the-art, our system uses a method of convex hull to recognize the hand gesture. In our user study, we involve 20 participants to examine the system performance, and find that our system is viable and usable with a success rate of 95%.