  Recognizing a face based on its attributes is an easy task for a human to
perform as it is a cognitive process. In recent years, Face Recognition is
achieved with different kinds of facial features which were used separately or
in a combined manner. Currently, Feature fusion methods and parallel methods
are the facial features used and performed by integrating multiple feature sets
at different levels. However, this integration and the combinational methods do
not guarantee better result. Hence to achieve better results, the feature
fusion model with multiple weighted facial attribute set is selected. For this
feature model, face images from predefined data set has been taken from
Olivetti Research Laboratory (ORL) and applied on different methods like
Principal Component Analysis (PCA) based Eigen feature extraction technique,
Discrete Cosine Transformation (DCT) based feature extraction technique,
Histogram Based Feature Extraction technique and Simple Intensity based
features. The extracted feature set obtained from these methods were compared
and tested for accuracy. In this work we have developed a model which will use
the above set of feature extraction techniques with different levels of weights
to attain better accuracy. The results show that the selection of optimum
weight for a particular feature will lead to improvement in recognition rate.
