Instantaneous search and comparison method for large-scale distributed palm vein micro-feature data

An instantaneous search and comparison method for large-scale distributed palm vein micro-feature data, includes the following steps:

• S1:
• feature extraction and calculation of a palm vein micro-feature image, specifically comprises:
• S1-1:
• building multi-scale palm vein feature spaces, let the original image be L0, building a downsample image pyramid sequence Sp={L0, L1, . . . , Ln-1}, where n is the total image number, c is the downsample factor;
• S1-2:
• palm vein image enhancement, implement multi-scale Gauss filters on each layer of the sequence Sp, then extract the vein on the filtered images, combine the vein images under different scale kernels, obtaining the vein image sequence {V0, V1, . . . , Vn-1};
• S1-3:
• extract the global and local features of the vein image sequence, combine the global and local features together to get the final feature F;
• S2:
• building feature database, specifically comprises:
• S2-1:
• bucket partition on the global features from the feature dataset, initialize z buckets randomly, classifying each global feature using cluster algorithm;
• S2-2:
• building an inverted index for the local feature, building a “local feature ID—user ID” pair for the local feature of each feature F, building index system for all the local features, grouping the local features that are close in distance as one class, and putting the user IDs with the same local feature class to the inverted recording list;
• S3:
• search and comparison, specifically comprises:
• S3-1:
• introduce the feature H to be compared;
• S3-2:
• get the bucket index sequence {id0, id1, . . . , idp-1} by using the p global features H0 of the feature H, then implement the search in the acquired p buckets parallelly, the searching process in each bucket comprises:
• 1) traverse all the local features of the feature H, find the t nearest neighbors of each local feature;
• 2) find the set Tm of user IDs corresponding to the t neighbors;
• 3) implement the intersection operation on all the IDs acquired from the m local features:
• Gid=T0∩T1∩ . . . ∩Tm-1={ID0,ID1, . . . ,IDj}
• obtaining the ID set of each bucket;
• S3-3:
• implement the union operation on ID set of each bucket, i.e.,
• G=G0∪G1∪ . . . Gp-1={g0,g1, . . . ,gj}
• get the final ID set G;
• S3-4:
• calculate the similarity score of feature H with each feature in set G;
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• vg
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• where, vg is the local feature vector of G, vp is the local feature vector of H, m is the number of local features;
• S3-5:
• select the one with the highest similarity score as the final matched target.