27. A method for object fingerprinting as set forth in claim 26 , further comprising acts of: extracting a relevant feature set from the potential object candidate using a fixed-basis function decomposition module using Haar wavelets; classifying the potential object candidate using a static classifier, thereby generating a classification category selected from a group consisting of a positive identification, a negative identification, a false positive identification, and a false negative identification, and where upon classification, the static classifier communicates the classification to the incremental learning module.
27. A method for object fingerprinting as set forth in claim 26 , further comprising acts of: extracting a relevant feature set from the potential object candidate using a fixed-basis function decomposition module using Haar wavelets; classifying the potential object candidate using a static classifier, thereby generating a classification category selected from a group consisting of a positive identification, a negative identification, a false positive identification, and a false negative identification, and where upon classification, the static classifier communicates the classification to the incremental learning module. 33. A method for object fingerprinting as set forth in claim 27 , wherein in the act of predicting a general class of the potential object candidate using a incremental learning module, the incremental learning module includes a learning-based cascaded classifier framework configured to perform and act of predicting the general class of an object, the cascaded classifier including a pre-learning cascade and a post-learning cascade, wherein the pre-learning cascade includes a front-end multi-layer back-propagation neural network and an initial back-end incremental learning algorithm, and wherein the post-learning cascade includes the multi-layer back-propagation neural network and an updated incremental Teaming algorithm.