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

• DOI: 10.48550/arxiv.1910.01219
• Publication Date: 2019-01-01
• Authors: Raj Gupta; Shresth Verma; KV Arya; Soumya Agarwal; Prince Gupta
• Keywords: FOS: Computer and information sciences; 03 medical and health sciences; 0302 clinical medicine; Computer Vision and Pattern Recognition (cs.CV); Computer Science - Computer Vision and Pattern Recognition; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

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