Bayesian
Tactile Face
1. Z. Wang, X. Xu, Baoxin Li, “Bayesian Tactile Face”, IEEE
Conference on Computer Vision and Pattern Recognition, 2008. CVPR 2008.
Abstract: Computer users with visual
impairment cannot access the rich graphical contents in print or digital media
unless relying on visual-to-tactile conversion, which is done primarily by
human specialists. Automated approaches to this conversion are an emerging
research field, in which currently only simple graphics such as diagrams are
handled. This paper proposes a systematic method for automatically converting a
human portrait image into its tactile form. We model the face based on
deformable Active Shape Model (ASM), which is enriched by local appearance
models in terms of gradient profiles along the shape. The generic face model
including the appearance components is learnt from a set of training face
images. Given a new portrait image, the prior model is updated through Bayesian
inference. To facilitate the incorporation of a pose-dependent appearance
model, we propose a statistical sampling scheme for the inference task.
Furthermore, to compensate for the simplicity of the face model, edge segments
of a given image are used to enrich the basic face model in generating the
final tactile printout. Link for downloading PDF file.
2.
Z. Wang, Baoxin Li, “A Bayesian Approach to Automated Creation of Tactile
Facial Images”, .
Abstract:
Portrait photos (facial images) play
important social and emotional roles in our life. This type of visual media is
unfortunately inaccessible by users with visual impairment. This paper proposes
a systematic approach for automatically converting human facial images into a
tactile form that can be printed on a tactile printer and explored by a user
who is blind. We propose a deformable Bayesian Active Shape Model (BASM), which
integrates anthropometric priors with shape and appearance information learnt
from a face dataset. We design an inference algorithm under this model for
processing new face images to create an input-adaptive face sketch. Further,
the model is enhanced by input-specific details through semantic-aware
processing. We report experiments on evaluating the accuracy of face alignment
using the proposed method, with comparison with other state-of-the-art results.
Furthermore, subjective evaluations of the produced tactile face images were
performed by 17 persons including six visually-impaired users, confirming the
effectiveness of the proposed approach in conveying via haptics vital visual
information in a face image.
3. N.
Li, Z. Wang, J. Yuriar, B. Li, “TactileFace:
A System for Enabling Access to Face Photos by Visually-impaired People”, (Live
demo), International Conference on Intelligent User Interfaces (IUI), Feb.,
2011.
See related media reports
below:
MSNBC
http://www.msnbc.msn.com/id/41624232/41626743
Discovery
News http://www.msnbc.msn.com/id/41624232/41626743
ABC News http://abcnews.go.com/Technology/printed-photos-blind/story?id=12951372