A nonlinear approach for face sketch synthesis and recognition

Authors:
Qingshan, Liu
Xiaoou, Tang
Hongliang, Jin
Hanqing, Lu
Songde, Ma

Summary:
This method uses LLE to probe sketch based on the pseudo sketch generated from the photos. The idea behind LLE is to compute neighbhor preserving mapping between high dimensiion data to the low dimension feature space (usually based on simple geometric intuition in this case this is complex). In the case of faces, patch based strategy is followed - the photos are divided into overlapping patches. The pseudo sketch and artist sketch is normalized by fixing the position of the eye. KNN is used to calculate the weights of patches - to provide smooth transition between patches k=5 is chosen. KNDA - non linear version of LDA is used to classify the sketch. KNDA provides a better classification rate than LDA and PCA.

Discussion:
Need to read the paper in detail and understand how the patch based transformation works. How does KNN help in the transformation.