Discriminative common vector based finger knuckle recognition

• This paper presents an effective finger knuckle print recognition approach.
• Discriminative common vector based method is used to achieve the recognition task.
• In test, the most representative public database is used. Recognition rate: 100%.
• In test, an established non-uniform database is also used. Recognition rate: 100%.
• It is attained to the aimed goal with 100% RR, and approximately 0 EER.

The main issue in personal authentication systems for military, security, industrial and social applications is accuracy. This paper presents a finger knuckle print (FKP) recognition approach to identity authentication. It applies a discriminative common vectors (DCV) based method to obtain the unique feature vectors, called discriminative common vectors, and the Euclidean distance as matching strategy to achieve the identification and verification tasks. The recognition process can be divided into the following phases: capturing the image; pre-processing; extracting the discriminative common vectors; matching and, finally, making a decision. In order to test and evaluate the proposed approach both the most representative FKP public databases and an established non-uniform FKP database were used. Experiments with these databases confirm that the DCV-based FKP recognition method achieves the authentication tasks effectively. The results showed the performance of the system in terms of the recognition rate had 100% accuracy for both training data and unseen test data.

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