Gesture Recognition and Hand Tracking for Anti-Counterfeit Palmvein Recognition

Abstract

At present, COVID-19 is posing a serious threat to global human health. The features of hand veins in infrared environments have many advantages, including non-contact acquisition, security, privacy, etc., which can remarkably reduce the risks of COVID-19. Therefore, this paper builds an interactive system, which can recognize hand gestures and track hands for palmvein recognition in infrared environments. The gesture contours are extracted and input into an improved convolutional neural network for gesture recognition. The hand is tracked based on key point detection. Because the hand gesture commands are randomly generated and the hand vein features are extracted from the infrared environment, the anti-counterfeiting performance is obviously improved. In addition, hand tracking is conducted after gesture recognition, which prevents the escape of the hand from the camera view range, so it ensures that the hand used for palmvein recognition is identical to the hand used during gesture recognition. The experimental results show that the proposed gesture recognition method performs satisfactorily on our dataset, and the hand tracking method has good robustness.