As one of the most successful applications of image analysis and understanding face recognition has recently gained significant attention. In this paper, we present a proposed system for detecting a face in a given input image, extracts features from it, measures several identifying parameters and stores them in a database. At a later date, in order to recognize an individual it will again perform the same steps and match the parameters with those in the database. This Face Detection and Recognition System shall be a really effective biometric identification tool.
- Arpan Chakraborty
- Jit Ray Chowdhury
- Abhra Chattopadhyay
- Ankit Choudhury
Different approaches have been tried by several groups, working world wide, to solve the problem of human face recognition. Many commercial products have also found their way into the market using one or the other technique. But so far no system / technique exists which has shown satisfactory results in all circumstances. A comparison of these techniques needs to be done. In this context, we will try to do a comparative study of the performances of three algorithms - Eigenfaces, Artificial Neural Networks, and Hidden Markov Model based methods for face recognition. Often the problem of face recognition is confused with the problem of face detection. The two are related problems but definitely not the same. The latter is often done as a preprocessing step to obtain the position of the face, in the image, to be recognized. We then present a System Design for our proposed system that shall perform both Face Detection and Face Recognition.
The following sequences of images and the corresponding texts illustrate the results obtained from our system.