Reconstruction Low- Resolution Image Face Using Restricted Boltzmann Machine

Low-resolution (LR) face images are one of the most challenging problems in face recognition (FR) systems. Due to the difficulty of finding the specific features of faces, the accuracy of face recognition is low. To solve this problem, some researchers are using an image reconstruction approach to improve the resolution of their images. In this research, we are trying to use the restricted Boltzmann machine (RBM) to solve the problem. Furthermore, a labelled face in the wild (lfw) database has been used to validate the proposed method. The results of the experiment show that the PSNR and SSIM of the image result are 34.05 dB and 96.8%, respectively

OPTIMIZED BIOMETRIC SYSTEM BASED ON COMBINATION OF FACE IMAGES AND LOG TRANSFORMATION

The biometrics are used to identify a person effectively. In this paper, we propose optimised Face recognition system based on log transformation and combination of face image features vectors. The face images are preprocessed using Gaussian filter to enhance the quality of an image. The log transformation is applied on enhanced image to generate features. The feature vectors of many images of a single person image are converted into single vector using average arithmetic addition. The Euclidian distance(ED) is used to compare test image feature vector with database feature vectors to identify a person. It is experimented that, the performance of proposed algorithm is better compared to existing algorithms