# A Multi-Face Challenging Dataset for Robust Face Recognition

arXiv: https://arxiv.org/abs/1810.01898 · HTML: https://mlsanity.com/paper/1810.01898/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2018.

## Built on by (most cited first, 2 of 2)

- [WSD: Wild Selfie Dataset for Face Recognition in Selfie Images](https://mlsanity.com/paper/2302.07245/) (2023)
- [A Multi-Task Deep Learning Framework for Pose-Invariant Face Recognition and Facial Landmark Localization](https://mlsanity.com/paper/2610.16849/) (2026)

## Abstract (arXiv)

Face recognition in images is an active area of interest among the computer vision researchers. However, recognizing human face in an unconstrained environment, is a relatively less-explored area of research. Multiple face recognition in unconstrained environment is a challenging task, due to the variation of view-point, scale, pose, illumination and expression of the face images. Partial occlusion of faces makes the recognition task even more challenging. The contribution of this paper is two-folds: introducing a challenging multiface dataset (i.e., IIITS MFace Dataset) for face recognition in unconstrained environment and evaluating the performance of state-of-the-art hand-designed and deep learning based face descriptors on the dataset. The proposed IIITS MFace dataset contains faces with challenges like pose variation, occlusion, mask, spectacle, expressions, change of illumination, etc. We experiment with several state-of-the-art face descriptors, including recent deep learning based face descriptors like VGGFace, and compare with the existing benchmark face datasets. Results of the experiments clearly show that the difficulty level of the proposed dataset is much higher compared to the benchmark datasets.