Disguised Face Identification (DFI) with Facial KeyPoints using Spatial Fusion Convolutional Network
This addresses the challenging problem of identifying faces under disguises for security and surveillance applications, representing an incremental improvement.
The paper tackles disguised face identification by introducing a deep learning framework that detects 14 facial keypoints and uses them for classification, achieving superior performance compared to state-of-the-art methods.
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.