An improved connected component based algorithm for face recognition

Abstract

Digital image processing is one of the key feature for security, authentication and authorization over the image. Image segmentation is an essential step to evaluate images and extract data from them. The separation of an image into significant structures, image segmentation is frequently an critical step in image analysis, visualization, object representation and many other image processing tasks. To identify and locating pointed discontinuities in an image, edge detection is needed. It is needed for image analysis for solving many complex problems. It also provides important image information can be used for image interpretation. In this work, firstly all the colored face images are converted into gray level images then apply connected component based segmentation algorithm on all gray level images to extract segments which are most suitable for recognition. In this segmentation algorithm, sobel edge detection algorithm and median filter for noise removal is used. Finally neural network is used to recognize the face images by taking suitable features (like standard deviation, mean gray value, center of mass and integrated density). Experiments are performed on IIITM_Gwalior, VITM dataset and FACE_94 dataset and results show that proposed algorithm recognition rate is better than PCA algorithm.