FaceParser – A new face segmentation approach and labeleddatabase
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2018-03-10 
https://doi.org/10.14419/ijet.v7i2.5.10043
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Face segmentation, pose estimation, gender classification, expression classification -
Abstract
Background and objective: A novel face parsing method is proposed in this paper which partition facial image into six semantic classes. Unlike previous approaches which segmented a facial image into three or four classes, we extended the class labels to six. Materials and Methods: A data-set of 464 images taken from FEI, MIT-CBCL, Pointing’04 and SiblingsDB databases was annotated. A discriminative model was trained by extracting features from squared patches. The built model was tested on two different semantic segmentation approaches – pixel-based and super-pixel-based semantic segmentation (PB_SS and SPB_SS).Results: A pixel labeling accuracy (PLA) of 94.68% and 90.35% was obtained with PB_SS and SPB_SS methods respectively on frontal images. Conclusions: A new method for face parts parsing was proposed which efficiently segmented a facial image into its constitute parts.
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References
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How to Cite
Khan, K., Ahmad, N., Uddin, I., Ehsan Mazhar, M., & Ullah Khan, R. (2018). FaceParser – A new face segmentation approach and labeleddatabase. International Journal of Engineering & Technology, 7(2.5), 1-3. https://doi.org/10.14419/ijet.v7i2.5.10043Received date: 2018-03-10
Accepted date: 2018-03-10
Published date: 2018-03-10