A Multi-Face Challenging Dataset for Robust Face Recognition

TL;DR

This paper introduces the IIITS MFace Dataset, a challenging collection of face images with variations and occlusions, and evaluates current face recognition methods, revealing their limitations in unconstrained environments.

Contribution

The paper presents a new multiface dataset with diverse challenges and assesses the performance of existing face recognition techniques on it, highlighting their robustness issues.

Findings

Deep learning face descriptors perform less effectively on the new dataset.

The dataset's difficulty surpasses existing benchmark datasets.

Current methods struggle with pose, occlusion, and illumination variations.

Abstract

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.