Face recognition: elastic relation encoding and structural matching

Abstract

Face recognition relies heavily on feature extraction and the classification of features in the process of pattern recognition. Existing methods tend to address the problem with some tradeoff between the speed and accuracy in the process. In this paper, a system known as elastic graph dynamic link model (EGDLM) is proposed to provide an effective and reliable solution. The model simplifies the traditional dynamic link model and integrates it with the active contour model for feature extraction. The complex facial pattern matching process is reduced to an elastic graph system matching of facial contours. A database of 1020 facial images was used for model testing and experimental results indicate an improvement of average recognition speed by more than 1000 times, and an overall recognition rate of over 85%.