Exemplar-Based Face Parsing

Brandon M. Smith     Li Zhang Jonathan Brandt     Zhe Lin     Jianchao Yang

University of Wisconsin – Madison

Adobe Research


In this work, we propose an exemplar-based face image segmentation algorithm. We take inspiration from previous works on image parsing for general scenes. Our approach assumes a database of exemplar face images, each of which is associated with a hand-labeled segmentation map. Given a test image, our algorithm first selects a subset of exemplar images from the database, Our algorithm then computes a nonrigid warp for each exemplar image to align it with the test image. Finally, we propagate labels from the exemplar images to the test image in a pixel-wise manner, using trained weights to modulate and combine label maps from different exemplars. We evaluate our method on two challenging datasets and compare with two face parsing algorithms and a general scene parsing algorithm. We also compare our segmentation results with contour-based face alignment results; that is, we first run the alignment algorithms to extract contour points and then derive segments from the contours. Our algorithm compares favorably with all previous works on all datasets evaluated.


Brandon M. Smith, Li Zhang, Jonathan Brandt, Zhe Lin, Jianchao Yang. Exemplar-Based Face Parsing, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June, 2013. [PDF 0.9 MB]


This work is supported in part by NSF IIS-0845916, NSF IIS-0916441, a Sloan Research Fellowship, a Packard Fellowship for Science and Engineering, Adobe Systems Incorporated, and an NSF Graduate Research Fellowship.

Experimental Dataset

Our supplementary segment annotations for the Helen face dataset are available in two sizes:

Update 2016-02-18: The segment labels for training image 2371615952_1 have been corrected in both the original and resized versions above.

For more information, please see the readme file here, which is also included with each download above.


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Supplementary Results

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Supplementary Video

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