Face Recognition by Expression-Driven Sketch Graph Matching

Authors:
Zijian, Xu
Jiebo, Luo

Abstract:
We present a novel face recognition method using automatically extracted sketch by a multi-layer grammatical face model. First, the observed face is parsed into a 3-layer (face, parts and sketch) graph. In the sketch layer, the nodes not only capture the local features (strength, orientation and profile of the edge), but also remember the global information inherited from the upper layers (i.e. the facial part they belong to and status of the part). Next, a sketch graph matching is performed between the parsed graph and a pre-built reference graph database, in which each individual has a parsed sketch graph. Similar to the other successful edge-based methods in the literature, the use of sketch increases the robustness of recognition under varying lighting conditions. Furthermore, with high-level semantic understanding of the face, we are able to perform an intelligent recognition process driven by the status of the face, i.e. changes in expressions and poses. As shown in the experiment, our method overcomes the significant drop in accuracy under expression changes suffered by other edge-based methods

Summary:
The face features are divided into 3 layers - face ( whole - high level feature) , part layers ( features like mouth,eyes,...) and sketch layer( features like line segment, blob, ...) .The sketch layer which is the low level features carries the knowledge from the higher level parent ( like position, length, orientation,...). This is helps in differential weighting of the features where the mouth can be given lower weight (to minimize difference in open and closed mouths) while giving greater weights to the eyebrows. This paper also provides a similarity measure to find the similarity between the sketches and photos.