ABSTRACT:

                             Human face detection and recognition play important roles in many applications such as video surveillance and face image database management. In our project, we have studied worked on both face recognition and detection techniques and developed algorithms for them. In face recognition the algorithm used is Robust  in which we recognize an unknown test image by comparing it with the known training images stored in the database as well as give information regarding the person recognized. These techniques works well under robust conditions like complex background, different face positions. These algorithms give different rates of accuracy under different conditions as experimentally observed.

In face detection, we have developed an algorithm that can detect human faces from an image. We have taken skin colour as a tool for detection. This technique works well for Indian faces which have a specific complexion varying under certain range. We have taken real life examples and simulated the algorithms in MATLAB successfully.Theresearcher addressed the problem of automated face recognition by functionally dividing it into face detection and face recognition. Different approaches to the problems of face detection and face recognition were evaluated, and five systems were proposed and implemented using the Matlab technical computing language. In the implemented frontal-view face detection systems, automated face detection was achieved using a deformable template algorithm based on image invariants. The deformable template was implemented with a perceptron. Unsupervised learning using Kohonen Feature Maps was used to create the Perceptron's A-units. The natural symmetry of faces was utilised to improve the efficiency of the face detection model. The deformable template was run down the line of symmetry of the face in search of the exact face location.

Key words: face, detection, recognition, Algorithm