  Categorisation of huge amount of data on the multimedia platform is a crucial
task. In this work, we propose a novel approach to address the subtle problem
of selfie detection for image database segregation on the web, given rapid rise
in number of selfies clicked. A Convolutional Neural Network (CNN) is modeled
to learn a synergy feature in the common subspace of head and shoulder
orientation, derived from Local Binary Pattern (LBP) and Histogram of Oriented
Gradients (HOG) features respectively. This synergy was captured by projecting
the aforementioned features using Canonical Correlation Analysis (CCA). We show
that the resulting network's convolutional activations in the neighbourhood of
spatial keypoints captured by SIFT are discriminative for selfie-detection. In
general, proposed approach aids in capturing intricacies present in the image
data and has the potential for usage in other subtle image analysis scenarios
apart from just selfie detection. We investigate and analyse the performance of
popular CNN architectures (GoogleNet, AlexNet), used for other image
classification tasks, when subjected to the task of detecting the selfies on
the multimedia platform. The results of the proposed approach are compared with
these popular architectures on a dataset of ninety thousand images comprising
of roughly equal number of selfies and non-selfies. Experimental results on
this dataset shows the effectiveness of the proposed approach.
