KurdFace-1000: A New Multi-Attribute Dataset for Facial Beauty Prediction Using Multi-task Learning - Ibrahem, Ali H.; Abdelazeez, Adnan M. (ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 2026)
In response to the demographic bias commonly observed in facial beauty prediction (FBP) models due to the underrepresentation of certain ethnic groups, KurdFace-1000 is introduced as a novel and balanced dataset developed as a field of research community. This dataset comprises 1000 color facial images of individuals from the Kurdistan Region of Iraq, evenly distributed across gender (500 male, 500 female) and facial expression (500 smiling, 500 non-smiling), and includes both frontal and profile views. Within the results of males, 200 are smiled and 300 are non-smiled and for the results of females, 300 are smiled and 200 are non-smiled. Moreover, each image is annotated with three key attributes: A beauty score on a [1–5] scale rated by five independent human raters, binary gender, and smile expression. KurdFace-1000 is the first dataset specifically designed to represent Kurdish facial features in FBP tasks, aiming to reduce ethnic bias and improve model performance for underrepresented populations. With a balanced structure and diverse annotations, the dataset supports various computational paradigms, including classification and regression, and serves as a critical step toward building culturally aware and inclusive deep learning models in FBP.