  Deep learning based approaches have been dominating the face recognition
field due to the significant performance improvement they have provided on the
challenging wild datasets. These approaches have been extensively tested on
such unconstrained datasets, on the Labeled Faces in the Wild and YouTube
Faces, to name a few. However, their capability to handle individual appearance
variations caused by factors such as head pose, illumination, occlusion, and
misalignment has not been thoroughly assessed till now. In this paper, we
present a comprehensive study to evaluate the performance of deep learning
based face representation under several conditions including the varying head
pose angles, upper and lower face occlusion, changing illumination of different
strengths, and misalignment due to erroneous facial feature localization. Two
successful and publicly available deep learning models, namely VGG-Face and
Lightened CNN have been utilized to extract face representations. The obtained
results show that although deep learning provides a powerful representation for
face recognition, it can still benefit from preprocessing, for example, for
pose and illumination normalization in order to achieve better performance
under various conditions. Particularly, if these variations are not included in
the dataset used to train the deep learning model, the role of preprocessing
becomes more crucial. Experimental results also show that deep learning based
representation is robust to misalignment and can tolerate facial feature
localization errors up to 10% of the interocular distance.
