In section 3.1,we limit the test scans must be frontal facial scans.But in the practical application,a facial scan with head pose is very commen,and so the head pose will result some facial data missing.Under this situation,if the methods still can maintain the high accuracy result of the Feature points extraction,we can say the methods are robust to the head poses.
From the local shape descriptors began to see,the shape descriptors such as spin im-age,DLP,Gaussian curvature,mean curvature,shape index etc. are basically pose invari-ant.But if one of them alone is not su¢ciently robust for detecting landmarks in facial datasets in a variety of poses.Thus the shape descriptor usually combine with other de-scriptors or combine an robust algorithm to make while system robust to the head pose.Xu [28]developed a system with the casecade …ltering and the e¤ective energy descriptor that we have introduced in section 3.1.The experiment result shows that the system is robust to the pose.But Xu did not illustrate vary detail,so we can not know how robust the system is.And in section 3.1,we also introduced the method Cond proposed in [29].The experiment shows the robust property to the head pose.However the rotation angle is 5± to 25± .
Apart from the shape descriptor,there are other methods robust to head pose have been proposed.Colbry and Jain [20] proposed an arbitrary pose algorithm of face veri…cation in 2005.The …rst step in the algorithm describes the detection of a core set of candidate points:the inner eyes,the nose tip and the outer eyes.They regarded that the easiest feature point to identify in arbitrary pose is the inside edge of an eye next to the bridge of the nose.This point has a shape index value which is close to zero and the area around this point has a consistent shape index value across all face images and proses.Hence the candidate of inner eye corners can be identi…ed by using these properties. And the second
easiest feature point to detect is the nose tip.They listed a criteria for …nding candidate nose points:Point closest to the scanner,point farthest to the right,point farthest to the left,point farthest from the vertical plane formed by points the points closest to the scanner and farthest to the left,point farthest from the vertical plane formed by points closest to the scanner and farthest to the right and point with the largest shape index.They used the criteria to …nd the nose tip in an arbitrary pose.The next step is using some constraints to eliminate and reduce the number of possible feature points.And the …nal step is to …nd three labeled points that correspond to three points labeled on the 3D facial model.These three points are used to calculate a transformation from the test scan to the model.Colbry used the ICP algorithm to calculates the best matching distance between the scan and the model for deciding the three feature point they want.The experiment result is not as good as the result in the frontal pose point detection.
After a short while,Lu and June [19] proposed a more robust method to the arbitrary pose.They regarded the nose tip is a distinctive point of the human face and it is insensitive to the facial expression change.If the pose of a face scan is represented by the angle of rotation,hence they suppose the nose tip still has the largest depth valus (z value) if projected onto the corrected pose direction.We can call it the directional maximum.We can see the examples of the direction maximum of the nose tip in Fig.21.We can simply state the method in the following:Lu and Jain quantized the 180 degree from -90 degrees to 90 degrees into N angles with equal angular interval (¢) just like Fig.22.Then they rotated the test scan and …nd the point with the maximum projection value along the corresponding pose direction as the nose tip candidate at all N directions.They believed that the true nos tip must be involved in the N candidate points.And next,they want to
…nd a the vertical pro…le in N camdidate points which has the most similar outline to the vertical pro…le on real nose tip in the training set as shown in Fig.23.They regarded the
most silmilar nose candidate point as the nose tip detection result.If the nose tip has been found,the pose angle has been known simutaneously.Hence they rotated the test scan by the pose angle and use the same method as the method proposed in [19] to extract the eye corners and the mouth tip.Although the method Lu and Jain proposed addresses the problem of pose variations and is tested against 300 multiview scans (00,§450) from 100 subjects,we …nd out that Lu and Jain limit the pose just rotate around the yaw.It means that the method only robust to changes in yaw for sure.Lu indicated that the changes in pitch would result in an expensive using brute force search.Hence if we can improve the search scheme,the method will be more robust to the arbitrary direction.
Figure21:Direction maximum of the nose tip [19].
Figure22:Pose angle quantization [19].
Figure23:Top:extracted nose pro…les ; midle:normalized and resampled nose
pro-…le;bottom : extracted pro…les overlaidis based on candidate.[19]
In the same way to analysis the facial pro…le,Faltemuer [21] proposed a method called Rotated Pro…le Signature to locate the nose tip automatically in the presence of pose or expression variation.He quantize the angle from 00 to 1800with equal angle interval 50.He rotated the test scan 50 about the vertical axis and extracted the points with largest x-coordinate to form the pro…le.The process is repeated until each pro…le is created in every rotate angle.Then a pro…le model are manually extracted from a single subject image to a known pro…le position.Faltemuer used the model to match every x-projection pro…le for searching the position with minimum matching score.The position with a minimum matching score is reported as the nose tip.All x-projection pro…les have their own position with a minimun matching score.Faltemuer de…ned the position with the global minimum matching score is exactly the nose tip with the pose angle.This system was tested by 7317 facial scan included 406 frontal and 6911 in various yaw and pitch angles.The experiment result reach almost 100% for only rotating about yaw.It also perform well when the angle of head up or drop needs to be lower than 450.When the rotation angle equals to § 60±,the result is not very accurate.It is caused by too large rotation angle so the e¤ective
information is missing a lot.Therefore We can know that the method wull lead to a bad result when the rotation angle is too large.And the rotated pro…le signature only used for the nose tip extraction although its e¤ect of robust tp head pose is vary well.
According to the above analysis,we can see the existing not many methods are robust to the head pose.Although there have been some methods show great robust result,they still can not deal with vary large pose variation such as the rotation angle bigger than 60± in [21].