Journal
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
Volume 28, Issue 11, Pages 3194-3207Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSVT.2017.2748379
Keywords
Convolutional neural network; deep learning; face alignment; face recognition
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Two approaches are proposed for cross-pose face recognition, one is built on the handcrafted features extracted from the 3D reconstruction of facial components and the other is built on the learned features from a deep convolutional neural network (CNN). As both approaches rely on facial landmarks for alignment across large poses, we propose the Fast Hierarchical Model (FHM) for locating cross-pose facial landmarks in real time. Unlike most 3D approaches that consider holistic faces, the first proposed approach considers 3D facial components. It segments each 2D face in the gallery into components, reconstructs the 3D surface for each component, and recognizes a query face by component features. The core part of the CNN-based approach is a modified VGG network. We study the performance with different settings on the training set, including the synthesized data from 3D reconstruction, the real-life data from an in-the-wild database, and both types of data combined. The two recognition approaches and the FHM are evaluated in extensive experiments and compared with state-of-the-art methods to demonstrate their efficacy.
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