Face Detection from Blurred Images based on Convolutional Neural Networks

Abstract

This paper proposes face detection from blurry and noisy images robustly and efficiently using convolutional neural networks. It has also been demonstrated that the method of face detection from blurred images utilizing convolutional neural networks is superior to other methods under consideration concerning precision-recall and discontinuity and continuity scores. Face detection is the infrastructure of face recognition; also, it includes but is not limited to the following topics: traffic surveillance, stereo videos, finding a criminal from large crowds in terrorist accidents, calibrated stereo images, face alignment of images from sensors with heterologous wavelengths, driving license photos, and animations. Some difficulties of this topic include the lack of joint datasets, movements (displacements), changing expression, intensive illumination, the likelihood of overfitting in the case of employing high-dimensional data, and the presence of numerous blurs and various aspects. There are multiple face detection methods from blurry images, such as blur kernel estimation and complex Fourier coefficients of a trained neural network, exerting metrics that define arduous patches. This paper executes face detection in noisy and blurry images applying convolutional neural networks that make face detection more applicable. This is due to exploiting two techniques for blur elimination and face detection on the foundation of convolutional neural networks.