  Biometric recognition is the process of verifying or classifying human
characteristics in images or videos. It is a complex task that requires machine
learning algorithms, including convolutional neural networks (CNNs) and Siamese
networks. Besides, there are several limitations to consider when using these
algorithms for image verification and classification tasks. In fact, training
may be computationally intensive, requiring specialized hardware and
significant computational resources to train and deploy. Moreover, it
necessitates a large amount of labeled data, which can be time-consuming and
costly to obtain. The main advantage of the proposed TinySiamese compared to
the standard Siamese is that it does not require the whole CNN for training. In
fact, using a pre-trained CNN as a feature extractor and the TinySiamese to
learn the extracted features gave almost the same performance and efficiency as
the standard Siamese for biometric verification. In this way, the TinySiamese
solves the problems of memory and computational time with a small number of
layers which did not exceed 7. It can be run under low-power machines which
possess a normal GPU and cannot allocate a large RAM space. Using TinySiamese
with only 8 GO of memory, the matching time decreased by 76.78% on the B2F
(Biometric images of Fingerprints and Faces), FVC2000, FVC2002 and FVC2004
while the training time for 10 epochs went down by approximately 93.14% on the
B2F, FVC2002, THDD-part1 and CASIA-B datasets. The accuracy of the fingerprint,
gait (NM-angle 180 degree) and face verification tasks was better than the
accuracy of a standard Siamese by 0.87%, 20.24% and 3.85% respectively.
TinySiamese achieved comparable accuracy with related works for the fingerprint
and gait classification tasks.
