# Individuals Identification Based on Palm Vein Matching under a Parallel Environment
## 2 Related Works
### Image Matching Based on Optical Flow  
Image alignment, registration, and correspondence are central topics in computer vision. Image alignment becomes even more difficult in the object recognition scenario, where the goal is to align different instances of the same object category [48]. Some authors have proposed image matching techniques inspired by optical flow methods, which are able to produce dense, pixel-to-pixel correspondences between two images [48; 49; 50; 51; 52; 55]. This approach to obtain the correspondence between two images is formulated as an estimation of a 2D flow field.  
On this context, the approaches in the state-of-the-art can be categorized in dense [48; 55] and sparse [49; 50; 51; 52] matching algorithm, based on which pixel level is used to compute the correspondence. Dense algorithms use the information of every pixel to obtain a dense correspondence, on the contrary, sparse methods employ sparse feature points.  
In the first category, DenseSIFT flow proposed by [PERSON] et al. [48] consists of matching densely sampled, pixel-wise SIFT features [54] between two images, while preserving spatial discontinuities. The matching process is simulated as an optimization objective by using a message passing scheme, the dual-layer loopy belief propagation algorithm [56], and a coarse-to-fine matching scheme. The approach introduced in [48] was tested on face recognition by the authors and it was improved by [PERSON] et al. [45] for finger-vein recognition. Moreover, the proposal in [55] extends coarse matching to dense matching by enforcing affine constraints and a variational flow refinement. Both approaches, and on the whole dense matching methods, have shown excellent performance but at the expense of high computation costs.  
On the other hand, sparse matching approaches consider the image as a collection of textural segments, each represented by local image patches. Generally, in order to find a correspondence for a patch, the closest patch is taken disregarding the relation with the other patches. [PERSON] and [PERSON] [49] propose a method called SparseFlow, which also uses the other patches, as they help at selecting the appropriate textural subspaces. Final correspondences are selected as patch pairs where each patch is the sparse match of the other in a bijection, that means the sparse matching works both ways. SparseFlow performs well for small to medium displacements at a low computational cost, being one of the fastest CPU methods. DeepMatching algorithm introduced by [PERSON] et al. [51] is another sparse method inspired by deep convolutional approaches. This technique computes the correspondence by using a multi-layer architecture, which breaks down patches into a hierarchy of sub-patches. It explicitly handles non-rigid deformations, with bounds on the deformation tolerance, and incorporates a multi-scale scoring of the matches. Additionally, [PERSON] et al. [52] introduce a simple but fast and powerful matching method, which works in a coarse-to-fine scheme, called _CPM_ (Coarse-to-fine PatchMatch). It combines an efficient random search strategy and a propagation procedure between the adjacent levels from top to bottom. The fast approximate structure proposed in [48] for the matching process avoids finding the matching of every pixel and leads to a significant speed-up with controllable accuracy.  
Our proposal addresses previous limitations of [45] for finger-vein recognition, which is based on dense correspondence method and is time-consuming. In our case, we introduce the use of a sparse matching algorithm to estimate the similarity between palm vein images. It improves the effectiveness and efficiency of the matching process by achieving state-of-the-art accuracy and running much faster than the baseline method.