Learning Continuous Face Representation with Explicit Functions

• URL: http://arxiv.org/abs/2110.15268v1
• Date: Mon, 25 Oct 2021 03:49:20 GMT
• Title: Learning Continuous Face Representation with Explicit Functions
• Authors: Liping Zhang, Weijun Li, Linjun Sun, Lina Yu, Xin Ning, Xiaoli Dong, Jian Xu, Hong Qin
• Abstract summary: We propose an explicit model (EmFace) for human face representation in the form of a finite sum of mathematical terms. EmFace achieves reasonable performance on several face image processing tasks, including face image restoration, denoising, and transformation.
• Score: 20.5159277443333
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: How to represent a face pattern? While it is presented in a continuous way in our visual system, computers often store and process the face image in a discrete manner with 2D arrays of pixels. In this study, we attempt to learn a continuous representation for face images with explicit functions. First, we propose an explicit model (EmFace) for human face representation in the form of a finite sum of mathematical terms, where each term is an analytic function element. Further, to estimate the unknown parameters of EmFace, a novel neural network, EmNet, is designed with an encoder-decoder structure and trained using the backpropagation algorithm, where the encoder is defined by a deep convolutional neural network and the decoder is an explicit mathematical expression of EmFace. Experimental results show that EmFace has a higher representation performance on faces with various expressions, postures, and other factors, compared to that of other methods. Furthermore, EmFace achieves reasonable performance on several face image processing tasks, including face image restoration, denoising, and transformation.

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