2 research outputs found
Open Source Face Recognition Performance Evaluation Package
Biometrics-related research has been accelerated significantly by deep
learning technology. However, there are limited open-source resources to help
researchers evaluate their deep learning-based biometrics algorithms
efficiently, especially for the face recognition tasks. In this work, we design
and implement a light-weight, maintainable, scalable, generalizable, and
extendable face recognition evaluation toolbox named FaRE that supports both
online and offline evaluation to provide feedback to algorithm development and
accelerate biometrics-related research. FaRE consists of a set of evaluation
metric functions and provides various APIs for commonly-used face recognition
datasets including LFW, CFP, UHDB31, and IJB-series datasets, which can be
easily extended to include other customized datasets. The package and the
pre-trained baseline models will be released for public academic research use
after obtaining university approval.Comment: Technical repor
When 3D-Aided 2D Face Recognition Meets Deep Learning: An extended UR2D for Pose-Invariant Face Recognition
Most of the face recognition works focus on specific modules or demonstrate a
research idea. This paper presents a pose-invariant 3D-aided 2D face
recognition system (UR2D) that is robust to pose variations as large as 90? by
leveraging deep learning technology. The architecture and the interface of UR2D
are described, and each module is introduced in detail. Extensive experiments
are conducted on the UHDB31 and IJB-A, demonstrating that UR2D outperforms
existing 2D face recognition systems such as VGG-Face, FaceNet, and a
commercial off-the-shelf software (COTS) by at least 9% on the UHDB31 dataset
and 3% on the IJB-A dataset on average in face identification tasks. UR2D also
achieves state-of-the-art performance of 85% on the IJB-A dataset by comparing
the Rank-1 accuracy score from template matching. It fills a gap by providing a
3D-aided 2D face recognition system that has compatible results with 2D face
recognition systems using deep learning techniques.Comment: Submitted to Special Issue on Biometrics in the Wild, Image and
Vision Computin