Disguised Face Identification (DFI) with Facial KeyPoints using Spatial Fusion Convolutional Network

TL;DR

This paper presents a deep learning framework that detects facial keypoints to improve disguised face identification, introducing new datasets and demonstrating superior performance over existing methods.

Contribution

It introduces a novel facial keypoint detection framework and two annotated datasets specifically for disguised face identification, advancing the state-of-the-art.

Findings

Facial keypoint detection outperforms existing deep networks.

The framework achieves higher accuracy in disguised face identification.

New datasets enhance training and evaluation of disguise detection methods.

Abstract

Disguised face identification (DFI) is an extremely challenging problem due to the numerous variations that can be introduced using different disguises. This paper introduces a deep learning framework to first detect 14 facial key-points which are then utilized to perform disguised face identification. Since the training of deep learning architectures relies on large annotated datasets, two annotated facial key-points datasets are introduced. The effectiveness of the facial keypoint detection framework is presented for each keypoint. The superiority of the key-point detection framework is also demonstrated by a comparison with other deep networks. The effectiveness of classification performance is also demonstrated by comparison with the state-of-the-art face disguise classification methods.