Object classification is a crucial yet challenging vision ability to perfect The fundamental
objective is to educate computers to understand visuals the same way humans do. Due to …

The performance of face analysis and recognition systems depends on the quality of the
acquired face data, which is influenced by numerous factors. Automatically assessing the …

Recognition in low quality face datasets is challenging because facial attributes are
obscured and degraded. Advances in margin-based loss functions have resulted in …

Learning discriminative face features plays a major role in building high-performing face
recognition models. The recent state-of-the-art face recognition solutions proposed to …

State-of-the-art face recognition models show impressive accuracy, achieving over 99.8%
on Labeled Faces in the Wild (LFW) dataset. Such models are trained on large-scale …

This paper unravels the potential of sketches for diffusion models addressing the deceptive
promise of direct sketch control in generative AI. We importantly democratise the process …

Face recognition systems have significantly advanced in recent years, driven by the
availability of large-scale datasets. However, several issues have recently came up …

We present a one-shot method to infer and render a photorealistic 3D representation from a
single unposed image (eg, face portrait) in real-time. Given a single RGB input, our image …

We propose residual denoising diffusion models (RDDM) a novel dual diffusion process that
decouples the traditional single denoising diffusion process into residual diffusion and noise …

To minimize the effects of age variation in face recognition, previous work either extracts
identity-related discriminative features by minimizing the correlation between identity-and …