Abstract
In general, the field of face recognition has lots of research that have put interest in order to detect the face and to identify it and also to track it. Many researchers have concentrated on the face identification and detection problem by using various approaches. The proposed approach is further very useful and helpful in real time application. Thus the Face Detection, Identification  which is proposed here is used to detect the faces in videos in the real time application by using the FDIT (Face Detection Identification Technique) algorithm. Thus the proposed mechanism is very help full in identifying individual persons who are been involved in the action of robbery, murder cases and terror activities. Although in face recognition the algorithm used is of histogram equalization combined with Back propagation neural network in which we recognize an unknown test image by comparing it with the known training set images that are been stored in the database. Also the proposed approach uses skin color extraction as a parameter for face detection. A multi linear training and rectangular face feature extraction are done for training, identifying and detecting.   Thus the proposed technique   is PCA + FDIT technique configuration only improved recognition for subjects in images are included in the training data.  It is very useful in identify a single person from a group of faces.   Thus the proposed technique is well suited for all kinds faces frame work for face detection and identification. The face detection and identification modules share the same hierarchical architecture. They both consist of two layers of classifiers, a layer with a set of component classifiers and a layer with a single combination classifier.  Also we have taken a real life example and simulated the algorithms in IDL Tool successfully.
Highlights
The face is our primary focus of attention in social life playing an important role in conveying identity and emotions
Developing a computational model of face detection and recognition is quite difficult because faces are complex, multidimensional and meaningful visual stimuli [2]
Here we propose a new approach which is based on the multi linear training and rectangular face feature extraction
Summary
The face is our primary focus of attention in social life playing an important role in conveying identity and emotions. PERSPECTIVE OF OUR WORK: The proposed approach is simple, fast and accurate which is been applied together as a single algorithm to provide better results under complex circumstances like face position, luminance variation etc. Each of these algorithms are been discussed one by one below. The proposed approach handle changes on the face image like lighting, complexity in the background, multiple faces in the image. The information for the detecting process incorporates with the parameter and reproduces the information itself These algorithms always find faces in the frames even though the frame based detectors gets fails. The knowledge of training can understand new faces that are entered in the training and it is always ready to integrate in the updating process [7]
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