Abstract

Facial landmarking locates the key facial feature points on facial data, which provides not only information on semantic facial structures, but also prior knowledge for other kinds of facial analysis. However, most of the existing works still focus on the 2D facial image which may suffer from lighting condition variations. In order to address this limitation, this paper presents a coarse-to-fine approach to accurately and automatically locate the facial landmarks by using deep feature fusion on 3D facial geometry data. Specifically, the 3D data is converted to 2D attribute maps firstly. Then, the global estimation network is trained to predict facial landmarks roughly by feeding the fused CNN (Convolutional Neural Network) features extracted from facial attribute maps. After that, input the local fused CNN features extracted from the local patch around each landmark estimated previously, and other local models are trained separately to refine the locations. Tested on the Bosphorus and BU-3DFE datasets, the experimental results demonstrated effectiveness and accuracy of the proposed method for locating facial landmarks. Compared with existed methods, our results have achieved state-of-the-art performance.

Highlights

  • Accurate and automatic facial landmark detection or face alignment is critical in face verification, face recognition, facial animation, facial expression recognition and other research

  • We propose transforming the 3D face landmarks’ estimation to detect the landmarks on five types of 2D facial attribute maps, including shape index map, normal maps and original range map that calculated on 3D geometry data

  • We propose a novel approach to estimate landmarks on 3D geometry data

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Summary

Introduction

Accurate and automatic facial landmark detection or face alignment is critical in face verification, face recognition, facial animation, facial expression recognition and other research. 2D face images are rather sensitive to some condition changes such as arbitrary pose and illumination variations. The reconstructed 3D face shape based on corresponding 2D face texture is still sensitive to illumination changes. Motivated by this challenge, the emergence of 3D facial data has provided an alternative to enhance the accuracy and efficiency of facial landmarks’ estimation

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