Except the pose variation,expression variation also a¤ects the result of extration of feature points.The expression,such as smile,angry,frown,open the mouth etc,will change the local shape of the face.That makes some features change their shape and even change their geometrical properties.We can …nd out that the nose tip,inner eye corners and outer eye corners barely change due to the expression and mouth is the region with the biggest change due to the exprssion.Therefore in the existing works,most of the methods claim that their system is robust to the expression variation,however,they just extract those feature points which is invariant to the expression variation.Cond [29] extracted the nose tip and the eye corners.Cheng [12] only extracted the nos tip.Colbry and Jain [20] claim that their system is robust to the expression variation.But their test scan only include two kinds of expression:natural and smile.In [18, 19],Lu and Jain also claim their method is robust to the expression but the test scan only contain natural and smile.In [30],Segundo use 4950 images of 557 subjects with variations of facial expression,resolution,pose,and other characteristics,such as di¤erent hair styles.He extracted not only nose tip and eye corners but the nose bases,and the experiment result reach a high accuracy.However the
nose base is like nose tip and eye corners,it barely move due to expression variation.
For mouth feature points extraction,Gorden [39] use the priciple curvature to …nd the ridge and the valley line on human face as shown in Fig.2 and use the ridge line to
…nd the feature points. Kim [40] used the principle curvature information to …nd the feature line on human face,and utilised the feature line to …nd the feature points such as mouth corners.Although,we do not …nd a paper use feature line to extract the contour of the mouth and extract the mouth corner.We think extracting the contour in real time based on the maximum curvature is robust to the expression variation and keep the shape characteristic to extract the mouth corners or mouth tips.
Chapter 4
Conclusion and Future work
In this stuty,we …rst give a widely introduction of local shape feature descriptors.The local feature descriptors make an important role in whole feature point extraction frame-work.According to di¤erent feature descriptors,there will exist a corresponding methods or algorithms to use the characteristics provided by feature descriptors to extract the saliant feature points.Although there exist many methods that have good result in ex-tractging feature points,most of the methods are limited just for the frontal facial scan.For the problem caused by the changing pose,Faltemier proposed a robust method but just for nose tip extraction and still can not address the pose change over sixty degree.For the problem caused by the expression variation,most of the existing works only extract the nose tip and eye corners which are barely impacted by the expression.The mouth corners are a¤ected by the expression a lot.The existing feature descriptors can not describe it completely in any expression.It makes the mouth corners extraction be a chanllenge in nonstatistical ways.In the future,we will combine sevaral di¤erent descriptors to extract the nose tip and eye corners and …nd out the most robust combination of the descriptors and the corresponding algorithm for solving the problems caused by the changing pose and the expression variation.We will also try to use the principle curvature to extract the
contour of mouth and then utilise the contour to extract the feature points,such as mouth corners and mouth tip,in any expression.
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