abstract

Generating face ink portrait from face photograph

Authors:

Pei-Ying Chiang,

Kuo-Hao Chang,

Tung-Ju HsiehAuthors Info & Claims

SA '15: SIGGRAPH Asia 2015 Posters

Article No.: 4, Page 1

https://doi.org/10.1145/2820926.2820933

Published: 02 November 2015 Publication History

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Abstract

The Chinese ink portrait requires sophisticated skills and the training for Chinese ink painting takes a long time. In this research, a Chinese portrait generation system is proposed to allow the user to convert face images to Chinese ink portraits. We search the image using Active Shape Model (ASM) and extract facial features from an input face image. As a result, a feature-preserved ink diffused image is generated. In order to produce a feature-preserved Chinese ink portrait, we use artistic ink brush strokes to enhance face contour constructed with the facial features. The generated portraits can be used to replace faces in an ink painting.

References

[1]

Dong, L., Lu, S., and Jin, X. 2014. Real-time image-based chinese ink painting rendering. Multimedia Tools and Applications 69, 3, 605--620.

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[2]

Milborrow, S., and Nicolls, F. 2014. Active shape models with sift descriptors and mars. VISAPP.

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[3]

Xie, N., h. Laga, Saito, S., and Nakajima, M. 2010. Ir2s: interactive real photo to sumi-e. In NPAR '10, ACM - Association for Computing Machinery Inc, ACM, 63--71.

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Index Terms

Generating face ink portrait from face photograph
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation
    2. Rendering

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Published In

SA '15: SIGGRAPH Asia 2015 Posters

November 2015

45 pages

ISBN:9781450339261

DOI:10.1145/2820926

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 November 2015

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SA'15

SA'15: SIGGRAPH Asia 2015

November 2 - 6, 2015

Kobe, Japan

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Overall Acceptance Rate 178 of 869 submissions, 20%

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Generating frontal view face image for pose invariant face recognition

Age-Invariant Face Recognition

Lighting-aware face frontalization for unconstrained face recognition