Limitations
Although SealNet produced promising results, there are still limitations
that need to be addressed. First, our SealNet software still requires
some manual work during the data collection process—after running the
automatic face detector, researchers are still required to manually
locate the eyes, nose and mouth in order for the program to
automatically align and chip the seal faces. Thus, one possible
improvement that we can implement in the future is to add a landmark
detector to be used in conjunction with the face detector. Secondly, to
generate training data, researchers must manually group multiple face
chips belonging to the same individual. Not only is this process
laborious, it may be also error-prone. A more sustainable approach would
be to implement a classifier; however, researchers would still be
required to manually check if the classification is accurate.
Although SealNet does well in closed-set classification, open-set
verification performance could be dramatically improved by reducing the
similarity scores between such seals. This success could be achieved in
two ways. First, changes in preprocessing methodology could yield
greater differentiation between very similar seals by removing lighting
and other environmental effects without affecting color pattern
information the way grayscaling does. Second, changes to our model
architecture could improve such performance. However, the inherent
complexity in any attempt to leverage specificity, while simultaneously
avoiding overfitting, presents a difficult balance which all recognition
models struggle to strike. Thus, the best approach to this problem would
be to maximize the quantity and quality of information available to the
model through preprocessing improvements prior to making changes to the
CNN architecture itself.