  Face recognition in images is an active area of interest among the computer
vision researchers. However, recognizing human face in an unconstrained
environment, is a relatively less-explored area of research. Multiple face
recognition in unconstrained environment is a challenging task, due to the
variation of view-point, scale, pose, illumination and expression of the face
images. Partial occlusion of faces makes the recognition task even more
challenging. The contribution of this paper is two-folds: introducing a
challenging multiface dataset (i.e., IIITS MFace Dataset) for face recognition
in unconstrained environment and evaluating the performance of state-of-the-art
hand-designed and deep learning based face descriptors on the dataset. The
proposed IIITS MFace dataset contains faces with challenges like pose
variation, occlusion, mask, spectacle, expressions, change of illumination,
etc. We experiment with several state-of-the-art face descriptors, including
recent deep learning based face descriptors like VGGFace, and compare with the
existing benchmark face datasets. Results of the experiments clearly show that
the difficulty level of the proposed dataset is much higher compared to the
benchmark datasets.
