Convolutional neural networks have been proven to have strong feature representation
ability; however, they often require large training samples and high computation that are …

Automated personal identification using vascular biometrics, such as from the finger vein
images, is highly desirable as it helps to protect the personal privacy and anonymity in …

Personal authentication is getting harder and harder in the internet of things (IoT). Existing
methods used for personal authentication, such as passwords and the two-factor …

Finger vein verification has received more attention recently due to its unique advantages.
However, most existing algorithms rely on handcrafted features, making them less robust to …

Most finger vein feature extraction algorithms achieve satisfactory performance due to their
texture representation abilities, despite simultaneously ignoring the intensity distribution that …

Deep convolutional neural networks (DCNN) have been applied successfully for finger vein
recognition and have achieved promising performance in the past three years. However …

Finger vein recognition has been considered one of the most promising biometrics for
personal authentication. However, the capacities and percentages of finger tissues (eg …

Finger-vein, as a secure and convenient biometric characteristic in nature, has been widely
studied for authentication in recent years. In this paper, we propose an efficient finger-vein …

Finger vein recognition is a biometric technology using finger veins to authenticate a person,
and due to its high degree of uniqueness, liveness, and safety, it is widely used. The …

Finger vein is a new and secure biometric for personal authentication due to its line-structure
network with abundant local and orientation features. However, these features cannot be …