Deep learning and Machine Learning project

The purpose of this project is to investigate the effectiveness of using image classification to improve search time in massive image galleries (e.g.,the Shutterstock image library has some 50 million images). Two applications of image search are: 1) biometrics authentication for customers who wish to get access to their financial records and 2) people who are interested in searching for an image of a certain person in a image gallery.
Given that image search is a very time-consuming process the key question is what can be done to improve efficiency. One approach that immediately comes to mind is whether a hash filter will help. Hash filters have been successfully used in minimizing search time in large textual databases and hence it is natural to ask whether they could be used to accelerate search in image databases.
This project will use the hypothesis that a hash function is effective. The question then becomes: how exactly is a hash filter built? Basically, we have two choices: use landmark points on the face as components of the filter or use high level features such as Age, Gender and Race (assuming that the use of low-level features such as pixels will be hopelessly inefficient). In this project we shall take the latter approach.