  Smartphone based periocular recognition has gained significant attention from
biometric research community because of the limitations of biometric modalities
like face, iris etc. Most of the existing methods for periocular recognition
employ hand-crafted features. Recently, learning based image representation
techniques like deep Convolutional Neural Network (CNN) have shown outstanding
performance in many visual recognition tasks. CNN needs a huge volume of data
for its learning, but for periocular recognition only limited amount of data is
available. The solution is to use CNN pre-trained on the dataset from the
related domain, in this case the challenge is to extract efficiently the
discriminative features. Using a pertained CNN model (VGG-Net), we propose a
simple, efficient and compact image representation technique that takes into
account the wealth of information and sparsity existing in the activations of
the convolutional layers and employs principle component analysis. For
recognition, we use an efficient and robust Sparse Augmented Collaborative
Representation based Classification (SA-CRC) technique. For thorough evaluation
of ConvSRC (the proposed system), experiments were carried out on the VISOB
challenging database which was presented for periocular recognition competition
in ICIP2016. The obtained results show the superiority of ConvSRC over the
state-of-the-art methods; it obtains a GMR of more than 99% at FMR = 10-3 and
outperforms the first winner of ICIP2016 challenge by 10%.
