Search Results for author:
Found 9 papers, 2 papers with code
Analyzing the Impact of Shape & Context on the Face Recognition Performance of Deep Networks
In this article, we analyze how changing the underlying 3D shape of the base identity in face images can distort their overall appearance, especially from the perspective of deep face recognition.
Driver Glance Classification In-the-wild: Towards Generalization Across Domains and Subjects
Distracted drivers are dangerous drivers.
In-the-wild Drowsiness Detection from Facial Expressions
However, developing drowsiness detection systems that work well in real-world scenarios is challenging because of the difficulties associated with collecting high-volume realistic drowsy data and modeling the complex temporal dynamics of evolving drowsy states.
LEGAN: Disentangled Manipulation of Directional Lighting and Facial Expressions by Leveraging Human Perceptual Judgements
Building facial analysis systems that generalize to extreme variations in lighting and facial expressions is a challenging problem that can potentially be alleviated using natural-looking synthetic data.
On Hallucinating Context and Background Pixels from a Face Mask using Multi-scale GANs
We propose a multi-scale GAN model to hallucinate realistic context (forehead, hair, neck, clothes) and background pixels automatically from a single input face mask.
Fast Face Image Synthesis with Minimal Training
Our method samples face components from a pool of multiple face images of real identities to generate the synthetic texture.
SREFI: Synthesis of Realistic Example Face Images
In this paper, we propose a novel face synthesis approach that can generate an arbitrarily large number of synthetic images of both real and synthetic identities.
Visual Recognition of Paper Analytical Device Images for Detection of Falsified Pharmaceuticals
These cards have different reagents embedded in them which produce a set of distinctive color descriptors upon reacting with the chemical compounds that constitute pharmaceutical dosage forms.
To Frontalize or Not To Frontalize: Do We Really Need Elaborate Pre-processing To Improve Face Recognition?
If a CNN is intended to tolerate facial pose, then we face an important question: should this training data be diverse in its pose distribution, or should face images be normalized to a single pose in a pre-processing step?
