177 research outputs found
A Survey on Ear Biometrics
Recognizing people by their ear has recently received significant attention in the literature. Several reasons account for this trend: first, ear recognition does not suffer from some problems associated with other non contact biometrics, such as face recognition; second, it is the most promising candidate for combination with the face in the context of multi-pose face recognition; and third, the ear can be used for human recognition in surveillance videos where the face may be occluded completely or in part. Further, the ear appears to degrade little with age. Even though, current ear detection and recognition systems have reached a certain level of maturity, their success is limited to controlled indoor conditions. In addition to variation in illumination, other open research problems include hair occlusion; earprint forensics; ear symmetry; ear classification; and ear individuality. This paper provides a detailed survey of research conducted in ear detection and recognition. It provides an up-to-date review of the existing literature revealing the current state-of-art for not only those who are working in this area but also for those who might exploit this new approach. Furthermore, it offers insights into some unsolved ear recognition problems as well as ear databases available for researchers
DiFaReli : Diffusion Face Relighting
We present a novel approach to single-view face relighting in the wild.
Handling non-diffuse effects, such as global illumination or cast shadows, has
long been a challenge in face relighting. Prior work often assumes Lambertian
surfaces, simplified lighting models or involves estimating 3D shape, albedo,
or a shadow map. This estimation, however, is error-prone and requires many
training examples with lighting ground truth to generalize well. Our work
bypasses the need for accurate estimation of intrinsic components and can be
trained solely on 2D images without any light stage data, multi-view images, or
lighting ground truth. Our key idea is to leverage a conditional diffusion
implicit model (DDIM) for decoding a disentangled light encoding along with
other encodings related to 3D shape and facial identity inferred from
off-the-shelf estimators. We also propose a novel conditioning technique that
eases the modeling of the complex interaction between light and geometry by
using a rendered shading reference to spatially modulate the DDIM. We achieve
state-of-the-art performance on standard benchmark Multi-PIE and can
photorealistically relight in-the-wild images. Please visit our page:
https://diffusion-face-relighting.github.i
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