PalmHashNet: Palmprint Hashing Network for Indexing Large Databases to Boost Identification

Abstract

Palmprint identification aims to establish the identity of a given query sample by comparing it with all the templates in the database and locating the most similar one. It becomes computationally expensive as the size of the database grows. It is because the number of comparisons becomes proportional to the number of templates stored in the database. The process needs to be accelerated to get a response in real-time, especially for large databases. This paper proposes a palmprint database indexing approach called PalmHashNet that generates highly discriminative embeddings to create a fixed-size candidate list for comparison to make identification a constant time operation. Acquired palmprint images are fed to the feature extraction network, which is pre-trained using softmax loss. A margin is added to the softmax loss to minimize the intra-class distance between samples belonging to the same class. It ensures that the features have high intra-class and low inter-class similarity. k-means and locality sensitive hashing (LSH) is investigated for index table creation. In this setting, cluster centers for k-means and hash values in the case of LSH serve as indices. The features are extracted for a given query palmprint and compared with the index values. The candidates lying in the most similar bin are retrieved for identification. The advantage of the proposed approach is that the query palmprint is compared with a small percentage of database instead of the whole. The proposed approach offers probabilistic guarantees for query identification in the selected bin. Experiments are conducted on four widely used palmprint databases viz . CASIA, IITD-Touchless, Tongji-Contactless and Hong Kong Polytechnic University Palmprint II (PolyU II). A penetration rate of 0.022%, 1.032%, 4.555%, and 0.39% at 100% hit rate is achieved on these databases, respectively. It makes the identification process approximately 4500, 96, 21, and 256 times faster on the respective databases.