Accelerated POP hashing for fast palmprint identification on large-scale databases

Feng, Yue; Ding, Haolun; Li, Bin; Chen, Xi

doi:10.23919/chicc.2017.8029132

Cited by 2 publications

(1 citation statement)

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“…orientation patterns that lie in the region of principal lines. An accelerated and improvised indexing technique that uses features generated from POP was proposed in [27]. A convolutional neural network (CNN) based feature extractor was proposed in [28].…”

Section: Related Workmentioning

confidence: 99%

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

Arora¹,

Kalra

Bhatia

et al. 2021

IEEE Access

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Palmprint identification aims to establish identity of a given query sample by comparing it with all the templates in the database and locating the most-similar one. It becomes compute-intensive as the number of comparisons becomes proportional to the size of the database. The process needs to be fastened to get 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. To minimize the intra-class distance between samples belonging to the same class a margin is added to the softmax loss. This ensures that the features have high intra-class and low inter-class similarity. k-means and locality sensitive hashing (LSH) are explored for index table creation. In this setting, cluster centers for k-means and hash values in case of LSH serve as indices. For a given query palmprint, the features are extracted and compared with the index values. The candidates lying in the most similar bin are retrieved for identification. The proposed approach gives probabilistic guarantees for the query to be identified 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) database. The proposed approach achieved a penetration rate of 0.022%, 1.032%, 4.555% and 0.39% at 100% hit rate thus, making the identification process approximately 4500, 96, 21 and 256 times faster on the considered databases respectively. The advantage of the proposed approach is that the query palmprint needs to be compared with only a small percentage of the database instead of the complete database.

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Section: Related Workmentioning

confidence: 99%