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Landmark-Based 3D Face Reconstruction from an Arbitrary Number of Unconstrained Images

In this paper, we propose a novel method for reconstructing 3D faces from 2D images. The method is characterized in three aspects. (i) It utilizes only geometric cues in the input images, i.e., 2D facial landmarks. (ii) It works for an arbitrary number of unconstrained images, both single and multiple images. (iii) It can effectively exploit complementary information in multiple images of varying poses and expressions. The method is implemented based on cascaded regression in shape space. We have evaluated the method on three databases and observed from the experimental results that (i) the reconstruction error is reduced as more images of different poses are used, (ii) the proposed method can obtain comparable reconstruction results by using state-of-the-art automated methods to detect the 2D landmarks, and (iii) the proposed method is robust to variations in facial expressions and image qualities.