A COUPLED STATISTICAL MODEL FOR FACE SHAPE RECOVERY FROM BRIGHTNESS IMAGES

[allahallah]

A Coupled Statistical Model for Face Shape
Recovery From Brightness Images
a statistical model that can be used to recover facial shape from brightness images of faces is developed here. principal components analysis on sets of parameters describing the contents of the intensity images and the facial shape representations are done to construct this model. This coupled model is able to generate accurate shape from out-of-training-sample intensity images. the image irradiance equation is commonly used to extract a ?eld of surface normals and then surface normals are integrated to get surface height function. The
idea is to construct a statistical model in which variations in
image brightness are linked to variations in facial shape

Facial Shape-From-Shading
SFS may be viewed as an integrated process in which surface height is recovered from an input image. The problems are that when inte-
grated, the concave/convex ambiguities in the needle-map can
lead to the distortion of the topography of the reconstructed face. a shape reconstruction method for bi-
laterally symmetric surfaces from a single image has been presented earlier. a novel equal-height contour propagation method for
solving the SFS problem was developed.

SURFACE SHAPE REPRESENTATIONS
Based on directional information,and based on height information, construction of statistical models of facial shape is done. We take orthogonal projection where the viewed surface is assumed to have
been projected into 2-D space of the image plane such that the
direction of the projection axis is opposite to that of the viewer.

for a full report with images refer:

[attachment=3277]

also visit:
http://znu.ac.ir/data/members/fazli_saei...130415.pdf

[adeela]

Abstract:
A coupled statistical model can be used to recover facial shape from brightness
images of faces. This project is a software tool that helps a user to recover the face from
the given input image. The input image may be either dark or bright image, where the
image is not so clear to identify. This tool is mainly aimed for the users, who are in need
to identify the face hidden in the input image, if present.
We achieve this by capturing variations in intensity and the surface shape
representations using a coupled statistical model. The model is constructed by performing
principal analysis on sets of parameters describing the contents of the intensity images
and the facial shape representations. By fitting the coupled model to intensity data, facial
shape is implicitly recovered from the shape parameters.
The Face Detection and shape recovery system provides a solution that can
automatically detect faces and shape position and recover the hidden image from the
given input image. The system takes photographic images as input. The output consists
of an array of rectangles, which corresponds to the location, and scale of faces detected
and comparison output. If it detects no faces, it results in informing that no face is
found