Many functions in our daily life require identity verification. Traditional identity verification methods, including passwords, ID cards, or other physical devices, all have their apparent weaknesses or inconvenience. Biometric-based identity verification techniques are popular and more secure than traditional methods but cannot prevent passive verification or provide liveness assurance. We have developed a new identity verification technique, called Concurrent Two-Factor Identification Verification (C2F-IV). It requires only a short video of the frontal face expressing a unique user-produced facial motion. It analyzes spatial facial features and facial motions concurrently to verify identity for various applications. Provisional Patent Application Number: 63135136. Click on the Video button below or here for explanation.

A golf swing requires full-body coordination and much practice to perform the complex motion precisely and consistently to make an effective swing. We are developing a unique motion analysis method to evaluate body motion. The primary goal is to evaluate how close the user’s body motion is to a reference or ideal motion. Using a deep neural network, we can analyze a golf swing video, determine if it was an effective or ineffective swing, and provide feedback about the specific body parts that need improvement. This evaluation result can be used as real-time feedback to help train the muscle memory to improve the performance and consistency. The same concept could be applied to other sports such as baseball and tennis or physical therapy. It could also be used as a tool to evaluate performance objectively for sports such as gymnastics and high diving.