Blur Detection in Identity Images Using Convolutional Neural Network

Today, the digital photos especially identity images are used in almost every form filling application. As user is uploading his/her photographs, the image quality issues will come up. Especially, if some automatic processing like face recognition is happening at back end. The image quality parameters include resolution/dimensions, size and blur. Apart form blur, rest can be checked by simple conditions. But, blur detection cannot be solved in a trivial way. Here we are proposing a deep learning based approach for detecting the blur in an image. We have prepared data comprising 250,000 identity images. Our aim is to provide a comprehensive list of clear and blur images before learning happens. Apart from naturally blurred images, we have added five types of artificial blur into the images for better generalization. We have done comparative analysis of our approach to statistical feature extractor i.e. BRISQUE which was trained on SVM. We have empirically selected the convolutional network with 4 layers. Performance evaluation shows the proposed approach is giving 98.05% on 113,000 images, and thus outperforming BRISQUE using SVM.