Face Recognition A One Shot Learning Perspective
Ability to learn from a single instance is something unique to the human species and One shot learning algorithms try to mimic this special capability. On the other hand, despite the fantastic performance of Deep Learning based methods on various image classification problems, performance often depends having on a huge number of annotated training samples per class. This fact is certainly a hindrance in deploying deep neural network based systems in many real life applications like face recognition. Furthermore, an addition of a new class to the system will require the need to re train the whole system from scratch. Nevertheless, the prowess of deep learned features could also not be ignored. This research aims to combine the best of deep learned features with a traditional One Shot learning framework. Results obtained on 2 publicly available datasets are very encouraging achieving over 90% accuracy on 5 way One Shot tasks, and 84% on 50 way One Shot problems.
2019 15th International Conference on Signal Image Technology Internet Based Systems (SITIS)