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.
Highlights
M ANY real-world applications call for the need of access control and security
This paper addresses the problem of accelerating the human identification in large palmprint databases
PROPOSED APPROACH This paper proposes a novel approach for indexing a palmprint database
Summary
M ANY real-world applications call for the need of access control and security. Contrary to traditional modes of authentication such as PIN, passwords, tokens that can be stolen or forged, biometrics provide a mechanism to authenticate an individual by analysing their physical (fingerprint, palmprint, iris, face etc.) or behavioral (gait, voice, signature etc.) traits. To address this, metricbased methods have been introduced that uses distancebased criterion to separate feature embeddings from different classes and bring them closer otherwise It proposes a novel metric-based palmprint feature extraction network that uses a function called ’additive margin loss’ [8] to supervise the training process. Contributions: 1) This paper proposes PalmHashNet, a novel indexing technique that learns compact feature vectors for palmprint images to facilitate faster identification. 2) Softmax loss with additive margin has been introduced to train the model for palmprint database indexing and to learn the feature vector embeddings simultaneously. This loss function ensures that the learned feature embeddings have low inter-class along with high intraclass similarity.
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