13-12-2012, 04:13 PM
A MATLAB based Face Recognition System using Image Processing and Neural Networks
A MATLAB based Face Recognition System using Image Processing.pdf (Size: 674.61 KB / Downloads: 404)
Abstract—
Automatic recognition of people is a
challenging problem which has received much attention
during recent years due to its many applications in
different fields. Face recognition is one of those challenging
problems and up to date, there is no technique that
provides a robust solution to all situations. This paper
presents a new technique for human face recognition. This
technique uses an image-based approach towards artificial
intelligence by removing redundant data from face images
through image compression using the two-dimensional
discrete cosine transform (2D-DCT). The DCT extracts
features from face images based on skin color. Featurevectors
are constructed by computing DCT coefficients. A
self-organizing map (SOM) using an unsupervised learning
technique is used to classify DCT-based feature vectors into
groups to identify if the subject in the input image is
“present” or “not present” in the image database. Face
recognition with SOM is carried out by classifying intensity
values of grayscale pixels into different groups. Evaluation
was performed in MATLAB using an image database of 25
face images, containing five subjects and each subject
having 5 images with different facial expressions. After
training for approximately 850 epochs the system achieved
a recognition rate of 81.36% for 10 consecutive trials. The
main advantage of this technique is its high-speed
processing capability and low computational requirements,
in terms of both speed and memory utilization.
INTRODUCTION
FACE recognition has become a very active area of
research in recent years mainly due to increasing
security demands and its potential commercial and law
enforcement applications. The last decade has shown dramatic
progress in this area, with emphasis on such applications as
human-computer interaction (HCI), biometric analysis,
content-based coding of images and videos, and
surveillance[2]. Although a trivial task for the human brain,
face recognition has proved to be extremely difficult to imitate
artificially, since although commonalities do exist between
faces, they vary considerably in terms of age, skin, color and
gender. The problem is further complicated by differing image
qualities, facial expressions, facial furniture, background, and
illumination conditions[3]. A generic representation of a face
recognition system is shown in Fig. 1.
This paper presents a novel approach for face recognition
that derives from an idea suggested by Hjelmås and Low[1]. In
their survey, they describe a preprocessing step that attempts to
identify pixels associated with skin independently of facerelated
features. This approach represents a dramatic reduction
in computational requirements over previous methods.
DISCRETE COSINE TRANSFORM
Overview
The discrete cosine transform is an algorithm widely used in
different applications. The most popular use of the DCT is for
data compression, as it forms the basis for the international
standard loss image compression algorithm known as JPEG[5].
The DCT has the property that, for a typical image, most of the
visually significant information about the image is
concentrated in just a few coefficients. Extracted DCT
coefficients can be used as a type of signature that is useful for
recognition tasks, such as face recognition[6,7].
Face images have high correlation and redundant
information which causes computational burden in terms of
processing speed and memory utilization.
CONCLUSION
This paper has presented a novel face recognition technique
that uses features derived from DCT coefficients, along with a
SOM-based classifier. The system was evaluated in MATLAB
using an image database of 25 face images, containing five
subjects and each subject having 5 images with different facial
expressions. After training for approximately 850 epochs the
system achieved a recognition rate of 81.36% for 10
consecutive trials. A reduced feature space, described for
experiment 2 above, dramatically reduces the computational
requirements of the method as compared with standard DCTfeature
extraction methods. This makes our system well suited
for low-cost, real-time hardware implementation. Commercial
implementations of this technique do not currently exist.
However, it is conceivable that a practical SOM-based face
recognition system may be possible in the future.