An intelligent agent for recognizing face under dim light conditions

Face Recognition plays a vital role in criminal detection is considered to be the most useful and eminent techniques for identifying a criminalized person. An intelligent system that recognizes such criminals from a large database out of which the dataset is considered under the various illumination conditions, is a challenging task. The idea of this research is to recognize such human faces under different dim light conditions. An intelligent agent helps in perceiving the environment where the captured faces subject to various illumination conditions and acts upon that environment. This can be illustrated by an intelligent approach towards integrating various techniques for the agent to perceive Illumination Normalization, Feature Extraction and Classification. The Illumination Normalization technique is useful for removing the dimness and shadow from the facial image which reduces the effect of illumination variations still retaining the necessary information of the face. The robust local feature extractor which is the gray-scale invariant texture called Local Binary Pattern (LBP) is helpful for feature extraction. K-Nearest Neighbor classifier is utilized for the purpose of classification and matching the face images from the database. Thus, the agent tends to identify the input face image from the available database after preprocessing the image and feature extraction. Various images for the agent from Yale-B database are used for testing to achieve the face recognition system.