  Choosing a decision threshold is one of the challenging job in any
classification tasks. How much the model is accurate, if the deciding boundary
is not picked up carefully, its entire performance would go in vain. On the
other hand, for imbalance classification where one of the classes is dominant
over another, relying on the conventional method of choosing threshold would
result in poor performance. Even if the threshold or decision boundary is
properly chosen based on machine learning strategies like SVM and decision
tree, it will fail at some point for dynamically varying databases and in case
of identity-features that are more or less similar, like in face recognition
and person re-identification models. Hence, with the need for adaptability of
the decision threshold selection for imbalanced classification and incremental
database size, an online optimization-based statistical feature learning
adaptive technique is developed and tested on the LFW datasets and
self-prepared athletes datasets. This method of adopting adaptive threshold
resulted in 12-45% improvement in the model accuracy compared to the fixed
threshold {0.3,0.5,0.7} that are usually taken via the hit-and-trial method in
any classification and identification tasks. Source code for the complete
algorithm is available at: https://github.com/Varat7v2/adaptive-threshold
