Abstract
Finger-vein recognition, which is one of the conventional biometrics, hinders fake attacks, is cheaper, and it features a higher level of user-convenience than other biometrics because it uses miniaturized devices. However, the recognition performance of finger-vein recognition methods may decrease due to a variety of factors, such as image misalignment that is caused by finger position changes during image acquisition or illumination variation caused by non-uniform near-infrared (NIR) light. To solve such problems, multimodal biometric systems that are able to simultaneously recognize both finger-veins and fingerprints have been researched. However, because the image-acquisition positions for finger-veins and fingerprints are different, not to mention that finger-vein images must be acquired in NIR light environments and fingerprints in visible light environments, either two sensors must be used, or the size of the image acquisition device must be enlarged. Hence, there are multimodal biometrics based on finger-veins and finger shapes. However, such methods recognize individuals that are based on handcrafted features, which present certain limitations in terms of performance improvement. To solve these problems, finger-vein and finger shape multimodal biometrics using near-infrared (NIR) light camera sensor based on a deep convolutional neural network (CNN) are proposed in this research. Experimental results obtained using two types of open databases, the Shandong University homologous multi-modal traits (SDUMLA-HMT) and the Hong Kong Polytechnic University Finger Image Database (version 1), revealed that the proposed method in the present study features superior performance to the conventional methods.
Highlights
Biometrics are methods used for user identification using the unique behavioral and physiological elements of the individual, including, inter alia, face, fingerprint, iris, and vein recognition.These biometrics are used in a variety of fields, such as system security, electronic payment, patient management in hospitals, access control, etc
The interest in multimodal biometric recognition using two or more types of biometrics combined is emerging. In relation to such a trend, in this research, finger-vein and finger shape multimodal biometrics based on a deep convolutional neural network (CNN) are proposed
This study proposes deep CNN-based finger-vein and finger shape multimodal biometrics that use finger-vein and finger shape information extracted simultaneously from finger images that were acquired with a single sensor-based single device using NIR light
Summary
Biometrics are methods used for user identification using the unique behavioral and physiological elements of the individual, including, inter alia, face, fingerprint, iris, and vein recognition. Each individual is identified after selecting representative models from feature samples of each class and comparing the similarities through a quantitative measurement before making the final decision Among these biometrics, several studies have been conducted in the past on finger-based recognition due to its various advantages. Methods that use unimodal recognition still experience difficulties in extracting patterns accurately for various reasons, such as illumination variations, finger positional variation, shading, misalignment, and quality changes caused by finger pressure with respect to the sensor, leading to decreased recognition performance To overcome these limitations, the interest in multimodal biometric recognition using two or more types of biometrics combined is emerging.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
