  This paper addresses the challenges of face attribute detection specifically
in the Indian context. While there are numerous face datasets in unconstrained
environments, none of them captures emotions in different face orientations.
Moreover, there is an under-representation of people of Indian ethnicity in
these datasets since they have been scraped from popular search engines. As a
result, the performance of state-of-the-art techniques can't be evaluated on
Indian faces. In this work, we introduce a new dataset, IIITM Face, for the
scientific community to address these challenges. Our dataset includes 107
participants who exhibit 6 emotions in 3 different face orientations. Each of
these images is further labelled on attributes like gender, presence of
moustache, beard or eyeglasses, clothes worn by the subjects and the density of
their hair. Moreover, the images are captured in high resolution with specific
background colors which can be easily replaced by cluttered backgrounds to
simulate `in the Wild' behaviour. We demonstrate the same by constructing IIITM
Face-SUE. Both IIITM Face and IIITM Face-SUE have been benchmarked across key
multi-label metrics for the research community to compare their results.
