uh i think it is my pleasure to present my work here and then a

nice young channel and um now working in france at C inside your uh the

subject of my paper is kernel similarity is that active appearance models for face recognition

uh first of all i'd beginning uh i want to use a few words um

active appearance models which is the base of my work uh it is quite to

the widely used to yin uh face recognition and the object tracking and sometimes for

uh medical image processing uh they in sexual uh the in central idea of this

matter is to uh build a model which contains pose the shape and texture information

uh of the training data uh and then when we have a new face to

recognise the model can generate a pen generates the

appearance of the new phase of id three shows so uh but us using this

matter that we can get the detail of the appearance of a new face and

also the uh location of the landmarks on the ball boulder of the base and

the dog runs uh so it is still quite powerful wow algorithm for sometimes for

the uh space

for the recognition of the face expressions except for uh and the this matter it

has it's lame eight so one of the most the important to a problem is

uh is that the quite sensitive to the illumination conditions uh that is so why

we want to our improvement

and in recent years there was quite a lot of researchers work on this problem

the illumination condition and the uh there is some idea is so what are some

of them the posted to at some action a some action no parametrise scene this

model which is the red delay corresponding to the illumination and to decide he rates

they depend it to this work by using this site here uh active appearance model

is able to generate the face is in a quite dark the illumination or a

very bright illumination but uh we know that the illumination that is not the worst

thing for the illumination when the uh illumination come from one side of the face

it to make that is half dark and half right and this is the more

complicated thing and the this idea that in the work for this case and someone's

supposed to uh apply a filter on the decoder for two can uh invariance condition

uh for example uh for here a transformation all couple filters uh and this matter

the also cost to lose some data use the information from the recognition images and

there is a kind of quite a tradition idea is to use some other october

no uh transformation instead of principal component and that is to extract the uh the

most important variables uh in that database and the although it is quite old about

so we believe a we find the and up operate the transformation uh it can

work for this case and this is the initial idea of our work

and in this page i want to presents the database aware working on it is

called a cmu pose illumination and expression database of human faces and descent also in

this data database is captured the in section and environment uh in the room there

is several cameras are wrong that the volunteer and several flashes are around him and

to make a different illumination conditions the flashes flash one by one to get the

illumination and the here i give out some examples in this database uh for each

person your uh us fourteen different pose of that is and for each post there

is twenty different illumination conditions and we can see from these pictures some of the

illumination it's quite complicated but complicated and heart to recognise

one so we decided to use this database uh with data statistic and the light

on the database uh here in this page the we show that histogram of the

euclidean distance between each of actors uh of the uh each vectors O and so

one is for here is the uh that terms of the shape and here that

is the distribution of the texture vectors uh we can see that so for the

shape vectors it is the close to a gaussian distribution and for that extra ones

uh it is so quite beautiful passion distribution uh this result is quite interesting and

the according to this result we decided to use the kernel to be able to

a similarity

uh similarity matrix the instead of the covariance matrix which is to use the in

pca uh have to see that it's uh occur no it's not such a new

id reading in this case requires the kernel pca came out to maybe twenty years

ago and it is directly used to eating active shape models which is the a

priori is work of active appearance model but that the alternatives and to continue to

use it in active appearance model requires it is very complicated to uh reconstruct the

phase and we construct that you major from the uh extraction features

but uh for the active appearance model it is very important to reconstruct the images

um here the proposed method we call it to a kernel similarity component analyse it

is quite different from the kernel pca but uh sometimes it seems quite similar with

each other and to mathematics star race is quite so it's clearly reading you might

paper he i don't want to uh talk about the can uh mathematical conclusions uh

just the procedure of this matter what uh is very simple which is to use

the kernel oh

we just use the kernel to build the uh similarity metrics and then calculates a

the principal component to from the uh

from the uh from up from the metrics it uh and then we get the

eigen faces which uh which represents a to the most important variation in the database

uh and here in this in this page what i want to this i want

to show how the eigen faces a facts to the variation of the model on

the left part is the uh result of the proposed the matter uh we can

see that for the first and the for the third feature uh it is obviously

control the illumination variance uh the illumination environments on the face but so for the

principal component to analyze um

the bar uh the variance is only between the genders are sometimes between the different

to uh shape of the face

so this result is tell us that so we have already choose the appropriate the

transformation um and here is the experimental results on the right C is the a

bit evaluation curves which what i don't like it's uh i like to see directly

the

the image is as we said before for uh yes the first column is the

result of the proposed method and the in the middle column eight is the result

of standard again and left column is the original you make use a which is

a to recognize and a as we said uh standard and it works well when

the illumination is not that complicated but so when it is how dark and half

right is uh the a and that and work but so the proposed method it

gives the quite good result

um we also applied this method in the a rotation of the phase in this

problem but the uh from the U matrix we can see that the improvement is

not that of years uh only for some certain case it's some uh some change

but not a lot

uh and here is the conclusion the proposed kernel similarity is the active appearance models

is robust to illumination and pose changes of the pc images with this signal them

at their depict the fitting procedure can accurately thing sizes bases for my right to

my dark affected by the illumination and say i have to emphasize that this method

has the quite big limit that sits requests uh applied set accuracy at a alignment

of the shape and texture vector if we couldn't do this it won't work would

and so that the next step for our work is that so we want to

make the matter to work on both the pose variation and illumination variations

and that this all thank you

estimate the parameter of the

so i can understand you clustering

yeah uh_huh

is your rights yeah there is a

i you mean you mean i think you mean this one yeah this one oh

yeah this is also part of our work

you see wow build a histogram of the uh of the mecc vectors and so

we uh and to that sir it shows the gaussian distribution so we just use

the uh

variance here

it is a portion and to the kernel we use is the portion so

we think here that it that is it's represents the variance of the gaussian distribution

so is that the

uh excuse me that's what is the other uh algorithm called you see

uh yes i think i heard that before

yeah actually the principal common to analyze is quite close to the uh independence the

common is except that that's a we try this matter what right do you depend

and one that's it doesn't work quite good

oh yeah

uh_huh

yeah actually what have uh convert opens up the uh ninety eight percent each of

the information