Numerical approaches in principal component analysis for face recognition using eigenimages

In recent years, through the process of intensive research, considerable progress has been made in area of automatic face recognition systems wherein computers can assist humans in many face recognition tasks. An automatic face recognition system reduces human intervention force and also removes the monotony factor and is perfectly suited for law enforcement activities like surveillance and general monitoring of public areas. This paper presents a novel and feasible method of implementing the face recognition technique based on eigenfaces. The method is intuitive, simple to express in mathematical terms, and flexible. Here we create a database of images and train these faces using the eigenface method to recognize a given face in the database. Another case, where the input image is non-facial is identified using our reconstruction algorithm developed. We applied preprocessing algorithms like smoothing transformation mean filtering, back ground elimination and local enhancement filter to bring the images in the database and the probe image into a standard, recognizable format. Based on its ability to distinguish between different faces, the system showed a maximum recognition rate close to 90%. The relationship between recognition accuracy, scale and rotation was also investigated.