期刊
IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 24, 期 9, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2015.2431445
关键词
Lucas-Kanade; active appearance models; dense image feature descriptors; face alignment; face fitting
资金
- Engineering and Physical Sciences Research Council [EP/J017787/1, EP/L026813/1, EP/H016988/1] Funding Source: researchfish
- EPSRC [EP/H016988/1, EP/L026813/1, EP/J017787/1] Funding Source: UKRI
Lucas-Kanade and active appearance models are among the most commonly used methods for image alignment and facial fitting, respectively. They both utilize nonlinear gradient descent, which is usually applied on intensity values. In this paper, we propose the employment of highly descriptive, densely sampled image features for both problems. We show that the strategy of warping the multichannel dense feature image at each iteration is more beneficial than extracting features after warping the intensity image at each iteration. Motivated by this observation, we demonstrate robust and accurate alignment and fitting performance using a variety of powerful feature descriptors. Especially with the employment of histograms of oriented gradient and scale-invariant feature transform features, our method significantly outperforms the current state-of-the-art results on in-the-wild databases.
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