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Choosing a decision threshold is one of the challenging job in anyclassification tasks. How much the model is accurate, if the deciding boundaryis not picked up carefully, its entire performance would go in vain. On theother hand, for imbalance classification where one of the classes is dominantover another, relying on the conventional method of choosing threshold wouldresult in poor performance. Even if the threshold or decision boundary isproperly chosen based on machine learning strategies like SVM and decisiontree, it will fail at some point for dynamically varying databases and in caseof identity-features that are more or less similar, like in face recognitionand person re-identification models. Hence, with the need for adaptability ofthe decision threshold selection for imbalanced classification and incrementaldatabase size, an online optimization-based statistical feature learningadaptive technique is developed and tested on the LFW datasets andself-prepared athletes datasets. This method of adopting adaptive thresholdresulted in 12-45% improvement in the model accuracy compared to the fixedthreshold {0.3,0.5,0.7} that are usually taken via the hit-and-trial method inany classification and identification tasks. Source code for the completealgorithm is available at: https://github.com/Varat7v2/adaptive-threshold

Adaptive Threshold for Online Object Recognition and Re-identification Tasks