Takashi Shinzaki

Irreversible fingerprint template using Minutiae Relation Code with Bloom Filter

Template protected fingerprint authentication techniques have been proposed, which enables to create an irreversible fingerprint template. In this paper, we propose a new irreversible template creation technique using Minutiae Relation Code(MRC) which can describe the minutiae information efficiently, and Bloom Filter which can realize the irreversibility feature. We evaluate the authentication accuracy and security factors such as Shannon Entropy and the number of attack possibilities using FVC2002 and FVC2004 DBs. As a result, our proposed method can achieve 1.8% EER in FVC2002 DB2 with 249 attack possibilities.

Vectorized fingerprint representation using Minutiae Relation Code

Minutiae-based vector representation algorithms have been proposed, which allow us not only to speed up matching tasks, but also to easily apply for various template protection techniques, such as Fuzzy Vault, Fuzzy Commitment, and BioHashing. In this paper, we propose a new vectorized fingerprint descriptor called Minutiae Relation Code(MRC), which consists of a set of vector-represented minutiae relation information between arbitrary minutiae. We also evaluate authentication performances using FVC2002 Database(DB1, DB2, DB3, DB4) and we show 0.82% Equal Error Rate(EER) in DB1, 0.82% in DB2, 2.71% in DB3, and 1.49% in DB4.

Privacy-Preserving Fuzzy Commitment for Biometrics via Layered Error-Correcting Codes

With the widespread development of biometrics, concerns about security and privacy are increasing. In biometrics, template protection technology aims to protect the confidentiality of biometric templates (i.e., enrolled biometric data) by certain conversion. The fuzzy commitment scheme gives a practical way to protect biometric templates using a conventional error-correcting code. The scheme has both concealing and binding of templates, but it has some privacy problems. Specifically, in case of successful matching, stored biometric templates can be revealed. To address such problems, we improve the scheme. Our improvement is to coat with two error-correcting codes. In particular, our scheme can conceal stored biometric templates even in successful matching. Our improved scheme requires just conventional error-correcting codes as in the original scheme, and hence it gives a practical solution for both template security and privacy of biometric templates.

On the fly finger knuckle print authentication

Finger knuckle print authentication has been researched not only as a supplemental authentication modality to fingerprint recognition but also as a method for logging into a PC or entering a building. However, in previous works, some specific devices were necessary to capture a finger knuckle print and users had to keep their fingers perfectly still to capture their finger knuckle. In this paper, we propose a new on the fly finger knuckle print authentication system using a general web camera. In our proposed authentication system, users can input their finger knuckle prints without needing their hand to remain motionless during image capture. We also evaluate the authentication accuracy of the proposed system, achieving an 7% EER under best conditions.

A Novel Local Feature for Eye Movement Authentication

Eye movement authentication technology has been proposed as a biometric modality, which enables to authenticate a user continuously, and has the counterfeit feature because of the difficulty of the imitation. By using the eye movement authentication, it is possible to realize an automatic authentication system as long as he/she is looking at a display to operate the device. However, the authentication accuracy is still low compared to the other traditional biometric modalities, such as fingerprint, face, and iris. In this paper, we propose the novel local eye movement feature to represent local differences of the captured time-series gazing position data, and we show the proposed feature can work as a complement feature against Mel Frequency Cepstrum Coefficients(MFCC), which is based on the local phase information of eye movement data.We show the classification rate improves from 61% to 82% in the BioEye2015 dataset by using our proposed method on the best case.

A novel hand-type detection technique with fingerprint sensor

In large-scale biometric authentication systems such as the US-Visit (USA), a 10-fingerprints scanner which simultaneously captures four fingerprints is used. In traditional systems, specific hand-types (left or right) are indicated, but it is difficult to detect hand-type due to the hand rotation and the opening and closing of fingers. In this paper, we evaluated features that were extracted from hand images (which were captured by a general optical scanner) that are considered to be effective for detecting hand-type. Furthermore, we extended the knowledge to real fingerprint images, and evaluated the accuracy with which it detects hand-type. We obtained an accuracy of about 80% with only three fingers (index, middle, ring finger).