Sejong Face Database: A Multi-Modal Disguise Face Database

TL;DR

This paper introduces the Sejong Face Database, a multi-modal disguised face dataset with various add-ons and spectra, to advance research in disguised face recognition and provide baseline detection results.

Contribution

It provides a comprehensive, multi-spectral face database with disguise variations, addressing the lack of such datasets for research and benchmarking.

Findings

Baseline face detection results across modalities

Analysis of disguise addon challenges

Dataset's potential to improve disguised face recognition

Abstract

Commercial application of facial recognition demands robustness to a variety of challenges such as illumination, occlusion, spoofing, disguise, etc. Disguised face recognition is one of the emerging issues for access control systems, such as security checkpoints at the borders. However, the lack of availability of face databases with a variety of disguise addons limits the development of academic research in the area. In this paper, we present a multimodal disguised face dataset to facilitate the disguised face recognition research. The presented database contains 8 facial add-ons and 7 additional combinations of these add-ons to create a variety of disguised face images. Each facial image is captured in visible, visible plus infrared, infrared, and thermal spectra. Specifically, the database contains 100 subjects divided into subset-A (30 subjects, 1 image per modality) and subset-B (70 subjects, 5 plus images per modality). We also present baseline face detection results performed on the proposed database to provide reference results and compare the performance in different modalities. Qualitative and quantitative analysis is performed to evaluate the challenging nature of disguise addons. The dataset will be publicly available with the acceptance of the research article. The database is available at: https://github.com/usmancheema89/SejongFaceDatabase.