IIITM Face: A Database for Facial Attribute Detection in Constrained and Simulated Unconstrained Environments

TL;DR

This paper introduces the IIITM Face dataset, capturing Indian faces with diverse emotions, orientations, and attributes, to improve face attribute detection in constrained and simulated unconstrained environments.

Contribution

The creation of the IIITM Face dataset with Indian faces, including emotions, orientations, and attributes, and its benchmarking in constrained and simulated wild scenarios.

Findings

Dataset includes 107 participants with 6 emotions and 3 orientations.

High-resolution images with customizable backgrounds.

Benchmark results for multi-label face attribute detection.

Abstract

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.