  Contactless 3D finger knuckle patterns have emerged as an effective biometric
identifier due to its discriminativeness, visibility from a distance, and
convenience. Recent research has developed a deep feature collaboration network
which simultaneously incorporates intermediate features from deep neural
networks with multiple scales. However, this approach results in a large
feature dimension, and the trained classification layer is required for
comparing probe samples, which limits the introduction of new classes. This
paper advances this approach by investigating the possibility of learning a
discriminative feature vector with the least possible dimension for
representing 3D finger knuckle images. Experimental results are presented using
a publicly available 3D finger knuckle images database with comparisons to
popular deep learning architectures and the state-of-the-art 3D finger knuckle
recognition methods. The proposed approach offers outperforming results in
classification and identification tasks under the more practical feature
comparison scenario, i.e., using the extracted deep feature instead of the
trained classification layer for comparing probe samples. More importantly,
this approach can offer 99% reduction in the size of feature templates, which
is highly attractive for deploying biometric systems in the real world.
Experiments are also performed using other two public biometric databases with
similar patterns to ascertain the effectiveness and generalizability of our
proposed approach.
