Feature-Based Lucas-Kanade and Active Appearance Models

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...

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Bibliographic Details
Published in:IEEE transactions on image processing 2015-09, Vol.24 (9), p.2617-2632
Main Authors: Antonakos, Epameinondas, Alabort-i-Medina, Joan, Tzimiropoulos, Georgios, Zafeiriou, Stefanos P.
Format: Article
Language:English
Subjects:
Active appearance model
Active Appearance Models
Algorithms
Biometric Identification - methods
Databases, Factual
dense image feature descriptors
Face
Face - anatomy & histology
face alignment
face fitting
Feature extraction
Humans
Image Processing, Computer-Assisted - methods
Integrated circuits
Lucas-Kanade
Optimization
Robustness
Shape
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Summary: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.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2015.2431445