  With the rise of handy smart phones in the recent years, the trend of
capturing selfie images is observed. Hence efficient approaches are required to
be developed for recognising faces in selfie images. Due to the short distance
between the camera and face in selfie images, and the different visual effects
offered by the selfie apps, face recognition becomes more challenging with
existing approaches. A dataset is needed to be developed to encourage the study
to recognize faces in selfie images. In order to alleviate this problem and to
facilitate the research on selfie face images, we develop a challenging Wild
Selfie Dataset (WSD) where the images are captured from the selfie cameras of
different smart phones, unlike existing datasets where most of the images are
captured in controlled environment. The WSD dataset contains 45,424 images from
42 individuals (i.e., 24 female and 18 male subjects), which are divided into
40,862 training and 4,562 test images. The average number of images per subject
is 1,082 with minimum and maximum number of images for any subject are 518 and
2,634, respectively. The proposed dataset consists of several challenges,
including but not limited to augmented reality filtering, mirrored images,
occlusion, illumination, scale, expressions, view-point, aspect ratio, blur,
partial faces, rotation, and alignment. We compare the proposed dataset with
existing benchmark datasets in terms of different characteristics. The
complexity of WSD dataset is also observed experimentally, where the
performance of the existing state-of-the-art face recognition methods is poor
on WSD dataset, compared to the existing datasets. Hence, the proposed WSD
dataset opens up new challenges in the area of face recognition and can be
beneficial to the community to study the specific challenges related to selfie
images and develop improved methods for face recognition in selfie images.
