Authors: YoungJae Park; SeokWoo Jang; JoongJae Lee; YangWeon Lee; GyeYoung Kim
Addresses: Department of Computing, Soongsil University, Sangdo 1-dong, DongJak-gu, Seoul, Republic of Korea ' Department of Digital Media Engineering, Anyang University, Anyang 5-dong, Manan-gu, Anyang-shi, Kyunggi-do, Republic of Korea ' Seocho R&D Campus, 221, Yangjae-dong, Seocho-gu, Seoul, Republic of Korea ' Department of Computer Information Engineering, Kunsan University, 1170, Daehangno, Gunsan, Jeonrabuk-do, Republic of Korea ' Department of Computing, Soongsil University, Sangdo 1-dong, DongJak-gu, Seoul, Republic of Korea
Abstract: In this paper, we suggest an image registration algorithm based on an ellipsoidal model with size-variable blocks that is similar in shape to a human face. While blocks are matched, the existing cylindrical model only considers that left-right curvature can accomplish a correct alignment on left and right images. However, registration errors are produced from up and down images as the cylindrical model does not reflect human head and jaw shape characteristics. The proposed algorithm exploits a block matching algorithm that uses size-variable blocks. The left-right and up-down curvature of the ellipsoidal face model is considered and input images are correctly aligned using their correlation. Next, we employ an image mosaic technique to generate a realistic facial texture from the aligned images. We stitch the images by assigning linear weights according to the overlapped regions and remove ghost effects to make the facial texture more natural. The experimental results show that the proposed algorithm can realistically generate 3D facial textures compared to other conventional methods.
Keywords: 3D facial texture; ellipsoidal models; curvature; image mosaics; registration algorithms; correlation; block matching; weighting factors; stitching; colour; modelling; image registration; human face.
International Journal of Computer Applications in Technology, 2013 Vol.46 No.1, pp.36 - 44
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 31 Dec 2012 *