Face sketch synthesis and recognition

Authors:
Xiaoou, Tang
Xiaogang, Wang

Summary:
The algorithm uses eigenfaces on sketch of the faces to find the weight vectors. This algorithm assumes that the transformation from the image of face to its sketch is linear. This is important because the mean representation of the face can converted to mean sketch using the linear transformation matrix. This linear assumption is also reasonable because the high pass filter can give rise to good sketch approximation.
The fiducial points in the sketch may not be the same when compared to the fiducial points on the face since the artist drawing the sketch may have exaggerated some features. This makes it difficult to find the linear transformation for the mapping of features and mapping of gray areas around the fiducial points. In order to remove this exaggeration and map the different fiducial points of the photos, The images are warped to mean image. affine transformation is performed on the face images and sketches to fix the different fiducial points. Eigenface method is then applied on the reduced sketch. To minimize the effect of transformation errors on the classification, the artist drawn sketch is classified using a bayesian classifier using the mahalanobis distance from the artist sketch vector to the eigenfaces of the reduced sketches of photos. The method than uses eigenfaces + bayes works better than PCA alone and PCA + eigenfaces.

Discussion:
This method again needs training data to find the eigenfaces.