This paper demonstrates two different fusion techniques at two different
levels of a human face recognition process. The first one is called data fusion
at lower level and the second one is the decision fusion towards the end of the
recognition process. At first a data fusion is applied on visual and
corresponding thermal images to generate fused image. Data fusion is
implemented in the wavelet domain after decomposing the images through
Daubechies wavelet coefficients (db2). During the data fusion maximum of
approximate and other three details coefficients are merged together.

In this work we investigate a novel approach to handle the challenges of face
recognition, which includes rotation, scale, occlusion, illumination etc. Here,
we have used thermal face images as those are capable to minimize the affect of
illumination changes and occlusion due to moustache, beards, adornments etc.
The proposed approach registers the training and testing thermal face images in
polar coordinate, which is capable to handle complicacies introduced by scaling
and rotation. Line features are extracted from thermal polar images and feature
vectors are constructed using these line.

This paper presents a concept of image pixel fusion of visual and thermal
faces, which can significantly improve the overall performance of a face
recognition system. Several factors affect face recognition performance
including pose variations, facial expression changes, occlusions, and most
importantly illumination changes. So, image pixel fusion of thermal and visual
images is a solution to overcome the drawbacks present in the individual
thermal and visual face images. Fused images are projected into eigenspace and
finally classified using a multi-layer perceptron.

In this paper fusion of visual and thermal images in wavelet transformed
domain has been presented. Here, Daubechies wavelet transform, called as D2,
coefficients from visual and corresponding coefficients computed in the same
manner from thermal images are combined to get fused coefficients. After
decomposition up to fifth level (Level 5) fusion of coefficients is done.
Inverse Daubechies wavelet transform of those coefficients gives us fused face
images.

This paper aims at determining the characteristics of a face image by
extracting its components. The FASY (FAce SYnthesis) System is a Face Database
Retrieval and new Face generation System that is under development. One of its
main features is the generation of the requested face when it is not found in
the existing database, which allows a continuous growing of the database also.
To generate the new face image, we need to store the face components in the
database. So we have designed a new technique to extract the face components by
a sophisticated method.

This work presents the application of weighted majority voting technique for
combination of classification decision obtained from three Multi_Layer
Perceptron(MLP) based classifiers for Recognition of Handwritten Devnagari
characters using three different feature sets. The features used are
intersection, shadow feature and chain code histogram features. Shadow features
are computed globally for character image while intersection features and chain
code histogram features are computed by dividing the character image into
different segments.

Automatic License Plate Recognition system is a challenging area of research
now-a-days and binarization is an integral and most important part of it. In
case of a real life scenario, most of existing methods fail to properly
binarize the image of a vehicle in a congested road, captured through a CCD
camera. In the current work we have applied histogram equalization technique
over the complete image and also over different hierarchy of image
partitioning. A novel scheme is formulated for giving the membership value to
each pixel for each hierarchy of histogram equalization.