Robust Face Recognition
18 papers with code • 0 benchmarks • 3 datasets
Robust face recognition is the task of performing recognition in an unconstrained environment, where there is variation of view-point, scale, pose, illumination and expression of the face images.
( Image credit: MeGlass dataset )
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Deep face recognition has achieved great success due to large-scale training databases and rapidly developing loss functions.
We evaluate our model on unsupervised person re-identification and pose-invariant face recognition.
Face image quality is an important factor to enable high performance face recognition systems.
Complete Face Recovery GAN: Unsupervised Joint Face Rotation and De-Occlusion From a Single-View Image
In addition, the lack of high-quality paired data remains an obstacle for both methods.
To perform unconstrained face recognition robust to variations in illumination, pose and expression, this paper presents a new scheme to extract "Multi-Directional Multi-Level Dual-Cross Patterns" (MDML-DCPs) from face images.
The capacity to recognize faces under varied poses is a fundamental human ability that presents a unique challenge for computer vision systems.
First, we propose a multi-task Convolutional Neural Network (CNN) for face recognition where identity classification is the main task and pose, illumination, and expression estimations are the side tasks.