Many forms of face recogniser work better if the facial image
patches used for training and querying are aligned to a common view.
This alignment allows for the recognition system to concentrate on
the appearance of the face without having to explicitly deal with
variations in the pose of the face. The
FaceAligners take in faces detected by a
FaceDetector as input, and output an image with
the aligned face rendered within it.
OpenIMAJ contains a number of face alignment options. Currently, these include:
An aligner that can align faces detected by the
FKEFaceDetector to a neutral pose by applying
an rigid affine transformation estimated from the mapping of
facial keypoints in the detected image to the points in a model
with neutral pose.
An aligner that warps a face detected by the
CLMFaceDetector to a neutral pose. The
alignment is non-rigid and warps each corresponding triangle of
the detected face to a model with neutral pose.
The identity aligner does no alignment; it just returns the
cropped face image from the detector. This is useful when
working with face datasets that contain pre-aligned images.
The mesh warp aligner performs a similar job to the
CLMAligner, but for
FKEDetectedFaces detected by the
FKEFaceDetector. A mesh is constructed over
the set of detected facial keypoints and a non-linear warp is
applied to project each keypoint to a canonical position within
a neutral pose.
The rotate and scale aligner maps faces detected by the
FKEFaceDetector to an aligned pose by
performing a rotation followed by a scaling.
The scaling aligner takes any type of
DetectedFace and scales the cropped face
image in the detection to a fixed size.