Absolute Face Recognition System using Machine Learning Approach from Blurred Images

Face Recognition is an important task in many domains to obtain the exact image from the pool of images. In general, blurred images are extremely challenging for the sensitive areas like law and order, defense, etc. Many face recognition techniques work well in normal images with various dimensions. In sensitive domains, the blurred image act as key evidence for the entire scenario. So, it is very important to find the original and accurate image from the blurred images. Representation of face, extraction of features and classification are the important steps in face recognition process. In existing models, Linear Binary Pattern (LBP) methods are used to recognize accurate images. LBP with Histogram (LBPH) is used to improve the detection performance of original images. Image sorting is done using the Point Spread Function Estimation on the blurred region and it helped to recognize faces with more accuracy. Extended Uniform Linear Binary Pattern method is used to reduce the dimensions to concentrate more on center pixels, with the use of the Viola-Jones algorithm and K-nearest neighbor (KNN) classifier for prediction.The proposed enhanced LBP approach assisted in achieving 94.7% accuracy in recognizing human faces from blurry images.