Vikriti-ID: A Novel Approach for Real Looking Fingerprint Data-Set Generation

Abstract

Fingerprint recognition research faces significant challenges due to the limited availability of extensive and publicly available fingerprint databases. Existing databases lack a sufficient number of identities and fingerprint impressions, which hinders progress in areas such as Fingerprintbased access control. To address this challenge, we present Vikriti-ID, a synthetic fingerprint generator capable of generating unique fingerprints with multiple impressions. Using Vikriti-ID, we generated a large database containing 500000 unique fingerprints, each with 10 associated impressions. We then demonstrate the effectiveness of the database generated by Vikriti-ID by evaluating it for imposter-genuine score distribution and EER score. Apart from this we also trained a deep network to check the usability of data. We trained a deep network on both Vikriti-ID generated data as well as public data. This generated data achieved an Equal Error Rate(EER) of 0.16%, AUC of 0.89%. This improvement is possible due to the limitations of existing publicly available data-set, which struggle in numbers or multiple impressions.