Cross-resolution face identification using deep-convolutional neural network

Low resolution (LR) and very low resolution (VLR) face images captured by surveillance cameras make automatic face recognition (AFR) a challenging task. The performance of an automatic face recognition system (AFRS) degrades when these types of face images are compared with high resolution (HR) gallery images. This paper has presented a face identification system called Cross-Resolution Face Identification System, to address this issue. It makes use of the Deep Convolutional Neural Network (DCNN) having different pooling operations to extract resolution robust features from high resolution, low resolution and very low resolution face images. The proposed system is evaluated on four face databases, namely, the ORL, the extended Yale face B, the LFW, and the Georgia Tech under three cross resolution environmental conditions based on resolution of probe images (i.e., high resolution to high resolution, low resolution to high resolution, and very low resolution to high resolution).The experimental outcomes exhibit the effectiveness of the proposed face identification system.