# Individuals Identification Based on Palm Vein Matching under a Parallel Environment
## 2 Related Works  
### Palm Vein Recognition  
A general outline of a palm vein recognition system is shown in Figure 2. Typically, it consists of four main processes: image acquisition, pre-processing, feature extraction, and recognition. The capture is made by infrared devices that obtain an image of the vein pattern on the hand. During the pre-processing, the image is segmented to obtain the region of interest (ROI), and different techniques are applied to improve the quality of the image. Feature extraction process computes the representation of the vein patterns, which will later be used in the process of automatic recognition. The main contributions of the literature related to the subject are focused on stages of feature extraction and recognition because these are decisive components in the accuracy of the results [15]. Palm vein recognition stands out among all the biometric systems because of its great level of recognition accuracy [15] and there are several works that propose multiple approaches [25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44]. Geometry-based approaches extract features from segmented blood vessels by using spatial methods such as Gabor filter [26] and vector grams of maximal intra-neighbor difference [25]. The main handicap of this type of techniques is that the segmentation and extraction of blood vessels is a very difficult process. It lacks precision and produces information loss on tiny and blurred pixels, which affects its performance. Methods based on statistical techniques characterize the texture of vein images at the pixel level of the image. Typically, these types of techniques include local binary patterns (LBP) [27; 28] and their different variants local derivative pattern (LDP) [29], Local Tetra Patterns (LTrP) [30], and local directional texture pattern (LDTP) [31]. In general, its performance is affected because the pixel-to-pixel processing is extremely sensitive to deformations. Subspace-based methods are adopted on machine learning to reduce or simplify the data structure by projecting the palm vein image into subspace built from training data. In this category, [PERSON] and [PERSON] [32; 33] propose two approaches based on Linear Discrimination Analysis (LDA), [PERSON] et al. [35] use principal component analysis (PCA), while [PERSON] [34] introduces Partial Least Square for palm vein identification, and [PERSON] et al. [36] improve subspace methods by using Radon Transform. In addition, some authors propose using local invariant features such as SIFT [37], RootSIFT [38], and SURF [39], as a solution to problems of rotation, scale, and axis changes. On the other hand, the feature matching process depends on the feature extraction used. Thus, matching methods can be categorized into three groups. Firstly, geometry-based matching compares points and lines features on the blood vessels by measuring metrics like Cosine Similarity Distance [30], Hamming Distance [26], and Euclidean Distance [37; 38]; second, feature-based matching [28; 40; 41] which performs better than geometry-based methods on low-quality images; third, and a little more recently, some authors propose machine learning techniques to improve classification results, such as probabilistic neural network [35], artificial neural network [42], and convolutional neural network (CNN) [43; 44]. As reported by [PERSON] et al. [15], most of the research works are focused on contactless palm vein recognition system. A palm vein contactless system is more suitable for real-world scenario. However, a contactless design system produces issues such as non-uniform illumination and affine transformation which is not suitable for some methods. Besides than that, palm vein images, as well as finger images, are severely affected by deformations, which occur due to scale change, axis change, and also affect the  
Figure 2: General scheme of a palm vein recognition system. accuracy of the system.