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Feature-based Lucas-Kanade and Active Appearance Models

Antonakos, Epameinondas; Alabort-i-Medina, Joan; Tzimiropoulos, Georgios; Zafeiriou, Stefanos P.


Epameinondas Antonakos

Joan Alabort-i-Medina

Georgios Tzimiropoulos

Stefanos P. Zafeiriou


Lucas-Kanade and Active Appearance Models are among the most commonly used methods for image alignment and facial fitting, respectively. They both utilize non-linear 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 multi-channel 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 HOG and SIFT features, our method significantly outperforms the current state-of-the-art results on in-the-wild databases.


Antonakos, E., Alabort-i-Medina, J., Tzimiropoulos, G., & Zafeiriou, S. P. (2015). Feature-based Lucas-Kanade and Active Appearance Models. IEEE Transactions on Image Processing, 24(9), doi:10.1109/TIP.2015.2431445

Journal Article Type Article
Publication Date May 8, 2015
Deposit Date Jan 29, 2016
Publicly Available Date Jan 29, 2016
Journal IEEE Transactions on Image Processing
Print ISSN 1057-7149
Electronic ISSN 1941-0042
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 24
Issue 9
Public URL
Publisher URL
Copyright Statement Copyright information regarding this work can be found at the following address:
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Copyright Statement
Copyright information regarding this work can be found at the following address:

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