# Gesture Recognition and Hand Tracking for Anti-Counterfeit Palmvein Recognition
## 2 Related Works  
Hand gesture recognition in an infrared environment has many advantages compared with methods using a visible environment. The hand gesture recognition in this paper is conducted in an infrared environment. Some researchers have used both a visible light environment and an infrared environment for hand gesture recognition. In 2014, [PERSON] and [PERSON] proposed a multi-mode hand gesture detection and recognition system [15]. They used both infrared and visible environment information. In 2021, [PERSON] et al. used reflected visible and infrared light signals for hand gesture recognition [16]. The simultaneous use of visible and infrared environments can enhance the system's robustness. However, sometimes, the two-camera mode results in difficulties with system development and additional costs. Recently, as devices for collecting hand gesture information, Microsoft Kinect (MK) and the Leap Motion Controller (LMC) are becoming popular [17; 18]. [PERSON] et al. proposed a multi-sensor fusion method based on the Hidden Markov Model (HMM) and Bidirectional Long Short-Term Memory Neural Network (BLSTM-NN), which combines MK and LMC to improve the performance of hand gesture recognition [19]. Special devices are not always available. Typically, a camera (a visible or infrared camera) is more cost-effective and has higher user acceptance. In recent years, deep learning has been widely used [20; 21]. As a result, most advanced gesture recognition methods are based on deep learning. In 2021, [PERSON] et al. proposed a lightweight hand gesture recognition model based on YOLO (You Only Look Once) v3 and DarkNet-53 [22]. [PERSON] et al. proposed the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) for multiple hand gesture classification [23]. With the advent of the big data era, deep learning methods commonly have higher accuracy than traditional methods [24]. Typically, fusion can effectively improve the accuracy [25] and achieve higher anti-counterfeiting, high versatility, and strong robustness [26; 27]. In 2022, [PERSON] et al. proposed an end-to-end fine-tuning method of the pre-trained Convolutional Neural Network (CNN) model with score-level fusion for hand gesture recognition [28]. The above works are summarized in Table 1.  
The system designed in this paper focuses on improving the anti-counterfeiting performance for subsequent palmvein recognition. It must track the hand to ensure that the hand used for palmvein recognition is identical to the hand used during gesture recognition. Hand tracking is mostly considered together with hand gesture recognition [29]. In 2013, [PERSON] et al. described a framework of real-time finger tracking technology using MK as an input device [30]. After that, some hand tracking studies were based on the LMC, such as [PERSON] et al. 's method [31] and [PERSON] et al. 's method [32]. In addition to MK and LMC, some other special sensors have also been used for hand tracking. In 2019, [PERSON] et al. proposed a virtual reality interactive interface for hand tracking based on Magnetic, Angular-Rate, & Gravity (MARG) sensors [33]. They used a set of infrared video cameras and MARG modules embedded in the users' gloves. [PERSON] et al. proposed a hand tracking system based on magnetic positioning [34]. They installed a magnetic node on each fingertip and two magnetic nodes on the back of their hands. They used a fixed array of receiving coils to detect magnetic fields and inferred the position and direction of each magnetic node from them. In addition to using special equipment, some researchers used videos captured with cameras for hand tracking. [PERSON] et al. combined CNNs with kinematic 3D hand models to solve the real-time 3D hand tracking problem based on monocular RGB sequences [35]. Their method is robust to occlusion and different camera viewpoints. [PERSON] et al. proposed a real-time hand tracking system that can generate accurate and low jitter 3D hand movements using four fisheye monochrome cameras [36].