LOW RESOLUTION FACE RECOGNITION USING DEEP LEARNING

Abstract

In recent years, face recognition systems have achieved impressive performance and results using variety of algorithms and methods but such methods often fail to recognize a face image of low resolutiom. Face Recognition(FR) degrades when faces are of very low resolution since many details about the difference between one person and another can only be captured in images of sufficient resolution. In order to have better face recognition in low resolution environment, our project uses a Convolutional Neural Network(CNN) model of a Residual Network Architecture to reconstruct the low resolution image into a higher resolution image and another CNN model to extract features from the newly reconstructed image to compare and recognize the face using a classifier. In case of the project, the low resolution image is taken of resolution 32X32 and the higher resolution image is of 128X128. The PSNR value for the super resolution model is 29.3256 dB and SSIM value is 0.7686. The accuracy of the Face Recognition model is 86.32% The performance of proposed method is evaluated on a custom face dataset using confusion matrix and it shows a decent precision and recall values.