.: Click here to download :.
Facial expressions convey non-verbal cues, which
play an important role in interpersonal relations. Automatic
recognition of facial expressions can be an important component
of natural human-machine interfaces; it may also be used in
behavioural science and in clinical practice. Although humans
recognise facial expressions virtually without effort or delay,
reliable expression recognition by machine is still a challenge.
We have developed a fast and efficient algorithm for facial expression
recognition. The algorithm consists of three main stages: eye
region locating stage, the eye detection stage and feature vectors extraction.
In the first stage, an effective
approach to fast location of the eye region is developed. In the second stage,
eye edge contour searching directed by knowledge is introduced in detail. Regional
image processing techniques are also described in the second stage. The main purpose
of the first stage is to locate the eye region roughly. The algorithm employed in the
second stage is restricted to application in just this region. It reduces the
complexity of the first stage and improves the reliability in the second stage.
Expression representation can be sensitive to translation, scaling, and rotation of
the head in an image. To combat the effect of these unwanted
transformations, the facial image may be geometrically
standardised prior to classification. This normalisation is
based on references provided by the eyes. Once eye regions has been detected,
in the third stage an invariant coordinate system is generated and extracted feature vectors
are used to train a neural network.
This code has been tested using the JAFFE Database, available at http://www.kasrl.org/jaffe.html.
Using 150 images randomly selected for training and 63 images for testing, without any overlapping,
we obtain a recognition rate equal to 83.14%. The semantic data ratings for this
database are available at http://www.kasrl.org/jaffe_info.txt.
Index Terms: Matlab, source, code, facial, expression, recognition, JAFFE, neural networks, network, eye, eyes, detection.
 |
Figure 1. Facial expressions |
||||||||||||||
|
A simple and effective source code for Knowledge-Based Eye Detection for Human Facial Expression Recognition. |
|||||||||||||||
|
|
Demo code (protected
P-files) available for performance evaluation. Matlab Image Processing Toolbox and Matlab Neural Network Toolbox are required.
|
||||||||||||||
|
Release |
Date |
Major features |
|||||||||||||
|
1.0 |
2009.02.18 |
|
|||||||||||||
We recommend to check the secure connection to PayPal, in order to avoid any fraud. This donation has to be considered an encouragement to improve the code itself. |
|||||||||||||||
Knowledge-Based Eye Detection for Human Facial Expression Recognition. Click here for
your donation. In order to obtain the source code you
have to pay a little sum of money: 150 EUROS (less
than 210 U.S. Dollars). |
|||||||||||||||
Once you have done this, please email us luigi.rosa@tiscali.it As soon as possible (in a few days) you will receive our new release of Knowledge-Based Eye Detection for Human Facial Expression Recognition. Alternatively, you can bestow using our banking coordinates:
|
|||||||||||||||
The authors have no relationship or partnership
with The Mathworks. All the code provided is written in Matlab
language (M-files and/or M-functions), with no dll or other
protected parts of code (P-files or executables). The code was
developed with Matlab 14 SP1. Matlab Image Processing Toolbox and Matlab Neural Network Toolbox are required.
The code provided has to be considered "as is" and it is without any kind of warranty. The
authors deny any kind of warranty concerning the code as well
as any kind of responsibility for problems and damages which may
be caused by the use of the code itself including all parts of
the source code.
