Feature Ensemble Networks with Re-Ranking for Recognizing Disguised Faces in the Wild

Arulkumar Subramaniam, Ajay Narayanan Sridhar, Anurag Mittal; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 0-0

Abstract


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.

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[bibtex]
@InProceedings{Subramaniam_2019_ICCV,
author = {Subramaniam, Arulkumar and Narayanan Sridhar, Ajay and Mittal, Anurag},
title = {Feature Ensemble Networks with Re-Ranking for Recognizing Disguised Faces in the Wild},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2019}
}