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
On Improving the Generalization of Face Recognition in the Presence of Occlusions
In this paper, we address a key limitation of existing 2D face recognition
methods: robustness to occlusions. To accomplish this task, we systematically
analyzed the impact of facial attributes on the performance of a
state-of-the-art face recognition method and through extensive experimentation,
quantitatively analyzed the performance degradation under different types of
occlusion. Our proposed Occlusion-aware face REcOgnition (OREO) approach
learned discriminative facial templates despite the presence of such
occlusions. First, an attention mechanism was proposed that extracted local
identity-related region. The local features were then aggregated with the
global representations to form a single template. Second, a simple, yet
effective, training strategy was introduced to balance the non-occluded and
occluded facial images. Extensive experiments demonstrated that OREO improved
the generalization ability of face recognition under occlusions by (10.17%) in
a single-image-based setting and outperformed the baseline by approximately
(2%) in terms of rank-1 accuracy in an image-set-based scenario.Comment: Technical Repor