Deep Attribute Driven Image Similarity Learning Using Limited Data

Abstract

In this work, we propose to derive the attribute specific similarity score for a pair of images using an existing parent deep model. As an example, given two facial images, we derive a similarity score for attributes like gender and complexion using an existing face recognition model. It is not always feasible to train a new model for each attribute, as training of deep neural network based model requires a large number of labelled samples to reliably learn the parameters. Hence, in the proposed framework a similarity score for each attribute is obtained as a weighted combination of all the hidden layer features of the parent model. The weights are attribute specific, and are estimated by minimizing the proposed triplet based hinge loss criteria over small number of labelled samples. Although generic, the proposed approach is developed in the context of a specific application to search for social media profiles of suspects of law enforcement agencies. To measure the effectiveness of our proposed approach, we have also created a social media dataset 'LFW Social (LFW-S)', corresponding to the Labeled Faces in the Wild (LFW) dataset. The key motivation behind our approach is not to improve upon the existing baseline methods but to reduce the overhead of generating a labeled dataset for learning new attribute. However, it is worth noting that the learnt attribute driven models performs at par with the existing baseline models on attribute driven ranking task.