The phrase "active appearance methods" is correct and usable in written English.
It is typically used in the context of computer vision and image analysis, referring to a statistical model used for shape and appearance representation.
Example: "The researchers implemented active appearance methods to improve facial recognition accuracy in their software."
Alternatives: "dynamic appearance models" or "active shape models".
We used an Active Appearance Model ('Materials and methods') to calculate an average face within any set of images, representing consistent shape and appearance features within the group.
To deal with the huge payload problem of privacy information hiding, the method uses statistical active appearance model (AAM) [12] for privacy information extraction and recovering.
In [21], an Active Appearance Model- (AAM-) based face synthesis method is applied for face recognition subject to relatively small pose variations.
The method is built upon Active Appearance Model (AAM) face tracking and feature extraction of global and local AAMs.
The Active Shape Models (ASM) [14] and Active Appearance Models (AAM) [15] are two popular appearance-based methods with statistical face models to prevent locating inappropriate feature points.
Flexible models include methods such as active shape models (ASM), active appearance models (AAM), and elastic graph matching (EGM).
A method is proposed, based on a modified active appearance model (AAM) approach, for unsupervised segmentation of the vocal tract from midsagittal RT-MRI sequences.
Imorphics' algorithm is based on the active appearance model; ScrAutoProstate's algorithm is a region-specific hierarchical segmentation method using discriminative learning.
Different methods are presented in this approach including[16, 17]: optical flow, Fisher linear discriminates, NN, active appearance models (AAMs), and Gabor filters.
Pixel intensities may be used directly as image features, usually with subsequent dimensionality reduction, e.g. by applying such methods as Locality Preserving Projections [9]; geometric features such as the distance between anthropometric landmarks [24]; Active Appearance Model parameters [6], and Local Binary Patterns [19, 28, 30].
