# Individuals Identification Based on Palm Vein Matching under a Parallel Environment
## 1 Introduction
Methods based on statistical techniques [27; 28; 29; 30; 31] characterize the texture of vein images at the pixel level of the image. Other works based on machine learning [32; 33; 34; 35; 36] adopt a subspace method to project the palm vein image into subspace built from training data. In addition, some authors propose using local invariant features [37; 38; 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, geometry-based matching [26; 30; 37; 38], feature-based matching [28; 40; 41], and machine learning [35; 42; 43; 44] are some of the techniques used by researchers to evaluate the performance of palm vein recognition. According to the review of [PERSON] et al. [15], most of the research works are focused on a contactless palm vein recognition system. A palm vein contactless system is more suitable for real-world scenarios. However, a contactless design system produces issues such as non-uniform illumination and affine transformation \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Biometric Technology** & **Security** & **Acceptability** & **Cost** & **Privacy** & **Size** \\ \hline Palm vein patterns & High & High & Medium & High & Medium \\ Fingerprint & Medium & High & Low & Low & Low \\ Face & Low & Low & Medium & Low & Medium \\ Iris & High & High & Medium to High & Medium & High \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative analysis of characteristics of different types of biometric technologies, from [24]. which is not suitable for some methods. Besides 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 accuracy of the system. Among the works available in the state-of-the-art, the approach of [45] stands out for its recognition performance for finger-vein recognition. The technique proposed in [45] demonstrates that deformations and displacements of finger-vein images can be used as discriminatory information, unlike the existing methods that try to prevent their influence. Figure 1 illustrates the deformation effects for different samples of palm vein images, where the variations of the vein patterns in form and scale are noticeable. In terms of methodology, [PERSON] et al. [45] incorporate an optimized correspondence to obtain the displacements of the pixels in two dimensions (2D) because of the deformations during the capturing process. The final similarity between two images of finger-veins is estimated from the texture of uniformity [47] of the displacement matrices obtained. In spite of the important contribution made in [45], their proposal has the disadvantage of involving a high computation in the process of comparison between images, which is more noticeable for palm vein images that are bigger than finger-vein images. Thus, their approach could not achieve real-time recognition or be implemented in the real world with large databases. In their work, the authors of [45] propose using dense correspondences of SIFT features, known as Dense SIFT flow [48], to obtain the pixel-based displacements between two vein images. This is the main reason of high computation in the matching process. However, other approaches interpolate the optical flow from sparse descriptor matching correspondences directly, known as sparse matching algorithms; these have recently shown great success in efficiency and accuracy [49; 50; 51; 52]. Among these methods, the approach presented by [PERSON] et al. [52] is highlighted because it introduces a fast approximate structure for the matching process, which avoids finding the correspondences of every pixel and leads to a significant speed-up with acceptable accuracy. Taking into account a large database, where it is necessary to identify individuals in real-time or for massive identification, an acceleration of the matching process of palm vein images is necessary.