# Gesture Recognition and Hand Tracking for Anti-Counterfeit Palmvein Recognition
## 1 Introduction  
The applications of biometrics are becoming more and more extensive [1]. However, due to the impact of the COVID-19, some biometric modalities are facing many challenges [2]. For example, traditional applications of face recognition require users to remove masks [3]. Some biometric applications are less affected by COVID-19, such as non-contact hand-based biometrics [4; 5], which is a popular modality due to its many obvious advantages [6; 7]:  
* Abundant discriminative information: The hand area is large and contains rich discriminative information with a high recognition accuracy;
* Few restrictions: The hand image can still be clearly recognized in low resolution, and even if the palm or fingers are slightly abraded, the accuracy and performance are still satisfactory;
* Low cost: A low-resolution camera can meet the resolution requirement;
* Strong privacy: It is not easy to collect a user's complete hand information without his/her authorization;
* High user acceptance: Hand features can be collected in non-contact mode, thus avoiding many problems associated with contact collection.  
Palmvein recognition is an important hand biometric modality used in infrared environments [8; 9], which has more advantages [10]:  
Small illumination effect: In the infrared environment, the interference of the illumination difference is smaller than that in the visible environment;  
Strong liveness detection: The infrared camera can take images of human veins, which can pass through the skin tissues of the human body for liveness detection;* Difficult abrasion and forgery: As the veins of the human body belong to the internal physiological characteristics, they are usually hidden under the epidermis of the human body, and there is basically no abrasion. Therefore, it is very difficult to forge veins;
* Few restrictions: The recognition accuracy is less affected by the scars and dirts on the hand surface.  
At present, most studies are focused on improving the accuracy and robustness of palmvein recognition, but do not attach enough importance to anti-counterfeiting [11; 12]. This paper aims to further improve anti-counterfeiting during palmvein recognition. We build an interactive system, which can recognize hand gestures and track hands for palmvein recognition in infrared environments. Usually, illegal users try to steal genuine users' biometric images to impersonate their identities [13; 14]. If the hand is required to make an \"unpredictable\" gesture, an illegal user cannot use a simple fake palmvein tool to pass the authentication test, because a simple tool cannot make a gesture like a real hand. Even if an illegal user can make a tool that has a certain hand gesture, he/she cannot predict the gestures commanded during authentication. Thus, this paper further improves the palmvein anti-counterfeiting method.  
The contributions of this paper are summarized as follows:  
* A hand gesture recognition algorithm is developed in infrared environment. Firstly, the hand gesture contours are extracted from infrared gesture images. Then, a deep learning model is used to recognize the hand gestures from these contours;
* A hand tracking algorithm is developed in an infrared environment, which is based on the detection of key points. 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 rest of this paper is organized as follows: The works related to gesture recognition and hand tracking are reviewed in Section 2. The proposed methods for gesture recognition and hand tracking are specified in Section 3. The experimental results are provided in Section 4. In Section 5, we conclude this work and elaborate on future improvement plans.