27 October 2017 Cascaded face alignment via intimacy definition feature
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Abstract
Recent years have witnessed the emerging popularity of regression-based face aligners, which directly learn mappings between facial appearance and shape-increment manifolds. We propose a random-forest based, cascaded regression model for face alignment by using a locally lightweight feature, namely intimacy definition feature. This feature is more discriminative than the pose-indexed feature, more efficient than the histogram of oriented gradients feature and the scale-invariant feature transform feature, and more compact than the local binary feature (LBF). Experimental validation of our algorithm shows that our approach achieves state-of-the-art performance when testing on some challenging datasets. Compared with the LBF-based algorithm, our method achieves about twice the speed, 20% improvement in terms of alignment accuracy and saves an order of magnitude on memory requirement.
© 2017 SPIE and IS&T
Hailiang Li, Hailiang Li, Kin-Man Lam, Kin-Man Lam, Man-Yau Chiu, Man-Yau Chiu, Kangheng Wu, Kangheng Wu, Zhibin Lei, Zhibin Lei, } "Cascaded face alignment via intimacy definition feature," Journal of Electronic Imaging 26(5), 053024 (27 October 2017). https://doi.org/10.1117/1.JEI.26.5.053024 . Submission: Received: 14 June 2017; Accepted: 6 October 2017
Received: 14 June 2017; Accepted: 6 October 2017; Published: 27 October 2017
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