Recognizing a person's face images with intentional/unintentional disguising effects such as make-up, plastic surgery, artificial wearables (hats, eye-glasses) is a challenging task. We propose a Feature EnsemBle Network (FEBNet) for recognizing Disguised Faces in the Wild (DFW). FEBNet encompasses multiple base networks (SE-ResNet50, Inception-ResNet-V1) pretrained on large-scale face recognition datasets (MS-Celeb-1M, VGGFace2) and fine-tuned on DFW training dataset. During the fine-tuning phase, we propose to use two novel objective functions, namely, 1) Category loss, 2) Impersonator Triplet loss along with two prevalent objective functions: Identity loss, Inter-person Triplet loss. To further improve the performance, we apply a state-of-the-art re-ranking strategy as a post-processing step. Extensive ablation studies and evaluation results show that FEBNet significantly outperforms the baseline models. © 2019 IEEE.