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Bull. Soc. Photogr. Imag. Japan. (2019) Vol. 29 No. 2: 27–34

Original Paper

Analysis of the Relationship between Age and Wrinkle from Facial Images Corrected for Surface Reflection
Ikumi Nomura*, Kaito Iuchi*, Mihiro Uchida*, Nobutoshi Ojima**, Takeo Imai**, Norimichi Tsumura***Abstract:

 e performed multi-resolution analysis using the wavelet transform on components of surface reflections from a facial image to identify
Wwrinkles and fine asperities. Additionally, by applying principal component analysis, we statistically analyzed relationships between distributions of the wrinkles and surface asperities, and actual ages. In previous research, the components of facial surface reflection were calculated as
for multi-resolution analysis, however, the uneven illuminance on the face affected the trend. Therefore, we proposed a method to transformthe luminance unevenness of facial images into a uniform distribution using signal processing, which contributes to the appropriate analysisof the surface reflection components on the face. As a result, compared with previous research, we could analyze the lighting unevenness withless influence of bias and acquire the distribution tendency of wrinkles and fine asperities in each direction.

Key words:

Facial image, Image processing, Wrinkles, Multi-resolution analysis, Principal Component Analysis (PCA)

1. Introduction
A human's face naturally receives the most attention comparedwith other body parts. When we observe the facial structure, skincolor or skin type, information can be obtained, such as sex, race,
individual features, emotion, apparent age, and health condition.
Features, such as skin color or facial structures, are called physicalfeatures. In contrast, features such as apparent age, or health condition based on physical features, are extra-physical features and are
identified as psychological features in this paper. A slight change inour facial features causes a significant difference in appearance.
Therefore, especially in the beauty industry, a simulator that modulates the physical features of the face and reproduces an arbitrary
appearance is required 1), 2). The purpose of this simulator is to predict
some effect of basic cosmetics and reproduce the face with the
make-up; therefore, it is based on the physical features, such as theface structure and skin condition, of the subject. Thus, it becomes
essential to consider the actual age of the subject, and how manydays the basic cosmetics are used. In general, a makeup simulator
installed in a cosmetic department, such as in a department store,
measures the condition of the customer's skin. By applying makeup
in the computer software to compensate for problems of skin colorand facial appearance, the simulator can assist in proposing cosmetic products that suit the customer.
Hirose et al. analyzed skin and face components, and based on its

results, they simulated the modulation of each feature to reproduce
any appearance of the face 3). They photographed facial images in alaboratory environment and constructed a facial image database thatconsisted of the three components (surface reflection, components
of internal reflection, facial landmarks) and the actual age. The tendency due to aging was able to be acquired by analyzing the threeelements statistically, and then the facial image was simulated at any
age by modulating features, as shown in Figure 1. The results of previous research are shown in Figure 2. The first line is the low-frequency component result, the second line is the horizontal high-frequency component result, the third line is the verticalhigh-frequency component result, and the fourth line is the diagonal high-frequency component. The distribution of the first to fifth

Figure 1 Th e results of aging simulation with three components related toappearance (skin pigment, surface reflection, and facial landmarks).
* This figure is cited from Hirose et al. 3)

Received 12th December 2019; Accepted 30th December 2019
*Graduate School of Science and Engineering, Ciba University; 1-33, Yayoi- cho, Inage-ku, Chiba-shi, Chiba 263-8522, Japan**Kao Company, Ltd., 5-3-28 Kotobukicho, Odawara City, Kanagawa 250- 0002, Japan***Graduate School of Engineering, Chiba University; 1-33, Yayoi-cho, Inage-ku, Chiba-shi, Chiba 263-8522, Japan

28

Bull. Soc. Photogr. Imag. Japan. Vol. 29 No. 2 (2019)principal component vector are shown in order from the left. In
particular, the surface reflection component represents fine asperities
and wrinkles on the skin surface, so the age-based features appear
prominently. In the previous research, according to the results of thestatistical analysis shown in Figure 2, a strong tendency for aging
appeared at prominent positions on the face, such as the forehead,nose, and cheeks. The component of the surface reflection depends
on the shape of the face, and conspicuous parts such as the forehead,nose line, and chin tip, are more strongly illuminated. In other words,
an illuminance difference throughout the face causes the wrinkles
and fine asperities to appear noticeably in only the strongly illuminated region. That is, the analysis was biased to the wrinkles and fineasperities of the skin from a restricted area of the face, and it did notshow an appropriate result of aging.
Therefore, in this paper, the illumination unevenness of the surface reflection was made uniform by signal processing, and feature

Analysis (PCA), and we obtained a relationship between its features
and actual ages in Section 4. The conclusions and future issues aredescribed in Section 5.

2. ApproachThis section explains the procedure to obtain feature values of
wrinkles and fine asperity distributions on the skin surface and analyze their relationship with respect to the whole face and actual age.
The procedure of the process is shown in Table 1.
2.1 Construction of the database of facial images
We constructed a database based on components of surface reflection, components of internal reflection, and actual ages. First, we
photographed 60 faces of Japanese women in a dark room. The experimental setup is shown in Figure 3. Blackout curtains surrounding the imaging system were used to eliminate the effect of ambient
light. There were four fluorescent lights surrounding the camera(a)Table 1 Analysis procedure
Step 1 Construction of the database of facial images

Step 2 Acquisition of facial landmarks, and morphing facial images to an
average face
Step 3 Obtaining components of surface reflectance by subtraction processing

Step 4 Correction for the components of surface reflectance (Proposed
method)Step 5 Extracting facial glosses, wrinkles and asperity distributions bymulti-resolution analysis

Step 6 Principal component analysis for the wrinkles and the distribution of fine asperities in the whole face

(b)

(b)

Figure 3 Th e experimental setup: (a) appearance of the imaging system and(b) overview of the imaging system.

15

subjects

maps of fine irregularities and wrinkles of the entire face were acquired. Then, by applying the analysis method used in the previous
study, we aimed to analyze the appropriate relationship between
wrinkles and fine asperities on the skin and actual age.
In this paper, Section 2 explains our approach, which consists ofthe construction of the database of facial images and the method to
remove the luminance unevenness of components of the surface reflection (proposed method). Additionally, we acquired the frequency
features depending on the direction by applying the multi-resolution analysis, which is the conventional method to correct surface
reflection. Section 3 documents the use of Principal Component(a)

The number of

Figure 2 Th
 e result of PCA applied to each frequency component in theconventional method: (a) color scale, which is the intensity anddirection of the principal component vector, and (b) the results of
the PCA in the conventional method.
* This figure is cited from 3)

10
5
0

10

20

30

40

50

60

Age groups

70

80

Figure 4 Age distribution of the subjects in this experiment image.

Ikumi Nomura, Kaito Iuchi, Mihiro Uchida, Nobutoshi Ojima, Takeo Imai, Norimichi Tsumura(a)Analysis of the Relationship between Age and Wrinkle from Facial Images Corrected for Surface Reflection 29(b)

Figure 6 Overview of the normalization process for a facial image.

(c)

(d)

Figure 5 E
 xamples of captured images and resized images: (a) a captured
facial image with specular light, (b) captured facial image without
specular light, (c) and (d) resized images from Figure 5 (a) and (b).

(Nikon D2H) for the light source. To prevent movement of the face,
we used support for the neck and head that was fixed on a chair
backrest.
The ages of the subjects were between 20 and 80 years old in 2015.
The distribution of age in the database is shown in Figure 4.
Figure 5 (a) and (b) show examples of the captured images withand without specular reflectance. For the preprocessing of the next
section, the captured images were resized to 512 * 512 resolution asshown in Figure 5 (c) and (d). Specular reflection on a captured image is removal by quickly setting polarization filters in front of thecamera and light sources perpendicularly to each other. The image
difference between these two images is the surface reflection component, which expresses the gloss, wrinkles and fine asperities of thewhole face such as pores. Therefore, the components of surface reflection were used to acquire features such as wrinkles and fine asperities. The actual surface reflection used was obtained by calculating the difference between the images with and without surface
reflection after the normalization processing of the facial image.
2.2 Acquisition of facial landmarks and morphing facial images toan average face
Facial images were normalized to remove influence caused by thevariation of individual facial shapes for accurate application of PCA.
For this reason, we used FUTON (Foolproof UTilities for facial
image manipulatiON system), which is a system to synthesize facial
images developed by Mukaida et al. 5). First, facial landmarks representing the facial structure were obtained and facial areas were extracted. Second, we morphed the shapes of facial images into an
image of an average face that was made from facial images in the
database. As a result, we obtained normalized facial images that keep
individual skin texture information. The overview of this process isshown in Figure 6. Figure 7 shows the results of applying the normalization to the captured facial images that are similar to Figure 5.

Figure 7(a)is a transformed image from a facial image with surface reflection, and Figure 7(b)is also a transformed image from a facial image without surface
reflection.
2.3 Obtaining components of surface reflectance using a subtraction processing
Subtraction processing was applied to Figure 7 to obtain components of surface reflection. The subtraction image has three channels(RGB). In order to reduce the data amount, the luminance component Y (YUV color space) from the RGB image is given by
Y=0.299R+0.587G+0.114B(1)where R, G, B is the pixel value from the original RGB image.

(a)

(b)

Figure 7 N ormalized capture images: (a) facial image with specular lightand (b) facial image without specular light.

(a)

(b)

Figure 8 C
 omponents of surface reflection image by subtraction processing:(a) a result of subtraction between Figure 7 (a) and (b), and (b) an
image after gamma correction for Figure 8 (a) for easy viewing.

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Bull. Soc. Photogr. Imag. Japan. Vol. 29 No. 2 (2019)

The extracted luminance component is shown in Figure 8 (a), andthen a gamma correction was applied to make it easier to see asshown in Figure 8 (b). By this process, the surface reflection component includes wrinkles and fine irregularities on the face. After theabove procedure, we acquired the components of surface reflection
and constructed a database with actual age.

[0.0-1.0]

Pixel value

0.2

0.1

0

Coordinate [0-512][0.0-1.0]

Pixel value

(a)

0.2
0.1
0
Coordinate [0-512](b)

Intensity2

1

0

Coordinate [0-512](c)

Figure 9 P
 roposed method as a one-dimensional signal: (a) original signal,a line of surface reflection shown in Figure 8 (a) in 400 rows, (b)smoothed signal in any parameter, (c) a divided signal from (a) to(b).

Figure 10 R esults of applying the proposed method to the grayscale imageof Figure 8 (a) which was converted by Equation (1).

2.4 Correction for the components of surface reflectance (Proposed method)
Based on the previous research, a multi-resolution analysis wasapplied in the next section to analyze the wrinkles and fine asperities
on the skin surface. The multi- resolution analysis was a method todecompose an image in direction-dependent frequency features in
the horizontal and vertical directions using a wavelet transform. If
the intensity of the illumination on the face was unevenness, as
shown in Figure 8 (b), the pixel values were changed. Therefore, we
proposed a method to remove the luminance unevenness of the surface reflection components by signal processing as the following
procedure. 1) Smooth the component of surface reflection by bilateral filter with arbitrary parameters empirically. 2) Calculate a value
in which the original value is divided by the value of 1). As a result,it was possible to acquire the surface reflection in which the unevenness of the illumination light is removed. This procedure is shown in
Figure 9 as a one-dimensional signal. The result of applying the proposed method to Figure 8 (a) is shown in Figure 10. As shown in
Figure 8 (a) and (b), before the correction, the illumination lit theforehead and cheeks of the face strongly, and the distribution of thewrinkles and fine asperities on the skin surface was dependent on
the distribution of luminance intensity. Alternatively, the proposed
method as shown in Figure 10 obtained wrinkles and fine asperities
not dependent on the distribution of the luminance intensity. We
acquired the corrected data of the surface reflection from which uneven lighting was removed by applying this process to all surface
reflections in the database.
2.5 
Extracting facial glosses and asperity distributions by amulti-resolution analysis
We used a multi-resolution analysis to extract facial glosses andfacial asperity distributions from the surface reflectance components. A two-dimensional discrete wavelet transform was used inthis analysis. By applying that transformation to the images, alow-frequency component and three kinds of high-frequency components were obtained. Each high-frequency component has three
directions, horizontal, vertical and diagonal. When we applied a
two-dimensional discrete wavelet transform to the components ofthe surface reflectance, the gross, i.e. the component of surface reflection on our faces, appears in the low-frequency component. The
facial asperities can be obtained in the high-frequency components,
but high- frequency components included individual variations,even after transformation to match the average face as described in
Section 3. The two-dimensional discrete wavelet transform is applied to the high-frequency components to average the individual
differences. Figure 11 shows an overview of these processes.
The left image in Figure 12 (a) shows an example of a result of the
proposed method and its simple pixel values. The center image in
Figure 12 (a) shows an example of the diagonal high-frequency
component that is applied to the two- dimensional discrete wavelet
transform. When we average its high-frequency component in order
to apply the two-dimensional discrete wavelet transform, sometimes
the averaged pixel value can be zero since they can have both positive and negative values; this is shown in the right-hand image inIkumi Nomura, Kaito Iuchi, Mihiro Uchida, Nobutoshi Ojima, Takeo Imai, Norimichi Tsumura

Figure 12 (a). We saved the information about the asperity distribution and calculated the absolute values of the high-frequency components before using the two-dimensional discrete wavelet transform to find the average value, as shown in Figure 12 (b). The original
images contain information about the positive or negative sign ofeach pixel. The results of the multiresolution analysis are shown in

Figure 11 Overview of a conventional multi-resolution analysis 2).

Analysis of the Relationship between Age and Wrinkle from Facial Images Corrected for Surface Reflection 31

Figure 13.
2.6 Principal component analysis for the wrinkles and fine asperity distributions in the whole faceIn this subsection, we obtained feature values of facial wrinkles
and asperity distributions by applying PCA to frequency components of the surface reflectance. PCA is a primary method to analyze
statistically for multivariable. By applying PCA to data groups, we
can obtain principal component vectors that indicate the directionin which the variance increases. The overview of PCA is shown in
Figure 14.
The 1st principal component vector is first defined to maximize
the variance of the data group. The 2nd principal component vector
is defined in such a way that it is orthogonal to the 1st principal
component vector. This analysis repeats up to the last principal component. By applying PCA, a n-dimensional l-th vector in a dataset
xln can be represented as the approximated vector l, which is defined by a principal component vector Pm and weight value vector
wlm as follows:

(

(a)

(b)

Figure 12 T wo-dimensional discrete wavelet transform of high-frequency
components:(a) general two-dimensional discrete wavelet transformation and(b) the method by Hirose et al. 3) for calculating an absolutevalue.

,

,

,⋯,

)=

(2)

PCA is able to approximately shrink the dimension by selecting
an essential principal component vector. Additionally, based on theweight of the principal component vector (the principal componentscore), we can determine how much is included in the data. In thisstudy, we first acquired principal component vectors to obtain thechange tendency of wrinkles and fine asperities of the whole face.
Next, we analyzed the correlation between age and the principal
component score. The PCA results are explained in the next section.

3. Results of the Principal Component Analysis
By applying PCA to each frequency component, we analyzed thecorrelation between the distribution of each frequency component

(a)

(b)

(c)

(d)

Figure 13 Th e results of the proposed multi-resolution analysis: (a) low-frequency, (b) horizontal high-frequency, (c) vertical high-frequency, and (d) diagonal high- frequency components.

Figure 14 Overview of the PCA.

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Bull. Soc. Photogr. Imag. Japan. Vol. 29 No. 2 (2019)

3.1 Principal component vector and contribution ratio
PCA was applied to the facial image, which has 64×64 pixels representing the frequency components of the surface wrinkles and fineasperities that were down- sampled by multi-resolution analysis.
One pixel was assigned as one element in the vector, and then asingle facial data was allocated as the one point in 4,096 (64×64)
dimensional spaces, respectively. We had 60 points in these dimensional spaces because there are sixty facial images in our database.
Based on PCA, we obtained 59 principal components in the frequency components of surface reflectance.
Figure 15 (b) shows the results of PCA applied to low-frequency,
horizontal high- frequency, vertical high-frequency and diagonal
high-frequency components in the conventional and proposed
method. Figure 15 (a) shows a color bar indicating the intensity anddirection of the principal components vector. The numbers at the top
left side of the images represent the number of principal components sorted by the contribution rate. Table 2 shows the contributionof the 1st to 4th principal components and Figure 16 shows the cumulative contribution of the principal components at each frequency. Comparing the conventional method in Figure 2 with the proposed method in Figure 15 (b), the proposed method was able toextract features from the whole face without depending on the local
area such as the forehead. Next, we focus on the results in the proposed method. We discuss the trend of the corrected frequency signal that has mixed low and high frequencies such as noticeable

(a)

(b)
Figure 15 Th
 e results of PCA applied to each frequency component in theconventional method: (a) color scale that is shown in intensityand direction of the principal component vector and (b) the results of the PCA in the proposed method.

1

The cumulative contribution ratio

and actual age. The principal component vector images and theircontribution rates are described in Subsection 3.1. The relationshipbetween the principal component score and actual age is describedin Subsection 3.2.

0.8

0.6

0.4

Low
Low
HorizontalHighhigh
Horizontal

0.2

0VerticalHighhighVerticalDiagonalHighhighDiagonal
1

4

7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58

The number of principal components
Figure 16 Th
 e contribution ratio of PCA in each component of the surface
wrinkles and fine asperities.

wrinkles and fine unevenness.
In the low-frequency components, it was predicted that featuresof wrinkles appear that did not depend on the direction. The output
signals show a nearly high frequency on the whole face. Alternatively, characteristic features were observed only on the facial contour
and around the nose. It is considered that low-frequency components appear in places where the contrast is visible, regardless of individual differences. The contrast of the facial contour depends on
the normalization process described in Section 2.2. Therefore, a noticeable change in the luminance easily occurs around the nose.
The horizontal high-frequency component detects changes in thevertical direction; as a result, the vectors appeared at the locations
where horizontal lines were distributed. The second row in Figure 15(b) shows that individual differences of vertical changes appear
strongly on the eyebrow, forehead, cheek, and other prominent features. Reviewing the 1st to the 3rd principal component, it is considered that there are many horizontal lines, especially on the forehead.
The vertical high-frequency component detects changes in thehorizontal direction; as a result, vectors appeared at the locations
where vertical lines were distributed. The third row in Figure 15 (b)shows that individual differences of vertical changes appear strongly
on the cheeks around the mouth, and so on. In a review of the 1st
and 3rd principal component, there are many vertical lines especially on the cheeks around the mouth.
The vertical high-frequency component detects changes in the
diagonal direction; as a result, the vectors appeared at the locations
where diagonal lines were distributed. The 1st and 2nd row in Figure 15 (b) shows that individual differences of diagonal changes appear strongly on the cheeks around the mouth, and so on. Based on
the 1st and 2nd principal components, it is considered that there aremany diagonal lines especially on the cheeks around the mouth. The
relationship and trends between the actual ages and each component
are explained in the next section.
3.2 Principal component score
Figure 17 shows the principal component scores of each subject

Ikumi Nomura, Kaito Iuchi, Mihiro Uchida, Nobutoshi Ojima, Takeo Imai, Norimichi Tsumura

Analysis of the Relationship between Age and Wrinkle from Facial Images Corrected for Surface Reflection 33

Figure 17 The principal component scores in each frequency component.

corresponding to the principal components of the respective frequency components shown in Figure 15 (b). Therefore, each figure
corresponds to the 1st, 2nd, 3rd, and 4th principal components fromleft to right. The horizontal axis shows the actual age (from ages 20
to 90), and the vertical axis shows each principal component score.
The straight line in the figure shows the regression line. Based onthese figures, we can determine whether there are correlations between principal components scores and actual ages.
Regarding the low-frequency component shown in the first row
of Figure 17, the tendency of change barely appears as previously
explained. The nose around the 1st principal component, which had
a slight tendency to change, tended to increase with age. Therefore,
the luminance change was likely to occur around the nose with age
from the low-frequency component.
The horizontal high-frequency components are shown in the second row of Figure 17. As previously demonstrated, the 1st to 3rd
principal component vectors showed the tendency to change theluminance on the eyebrow, forehead, and cheek. The 1st principal
component indicates that the individual difference in the change inluminance is the greatest around the eyebrows, but its correlation
coefficient was only 0.0197 as shown in Table 2. Therefore, there isalmost no correlation between changes in luminance in the eyebrows and actual age. The 2nd and 3rd principal component showsthat the individual difference in the change in luminance was thelargest around the forehead and cheeks, and its correlation coefficients were 0.5793 and 0.5890, respectively. Therefore, based on thehorizontal high-frequency components, the luminance change inTable 2 C ontribution ratio in each component of the surface wrinkles
and fine asperities
1st

Low

0.0657

Vertical High

0.0560

Horizontal High 0.0914

Diagonal High

0.0655

2nd

0.04530.0636

0.04680.05193rd

0.04060.04400.03740.03844th

0.03750.0346

0.03640.0344the horizontal direction was likely to occur on the forehead andcheeks with age.
Regarding the vertical high-frequency components shown in thethird row of Figure 17, the 1st principal component vectors especially showed the tendency to change the luminance on the cheeks
around the mouth, with a correlation coefficient of 0.5428. Therefore, based on the vertical high-frequency components, the luminance change in the vertical direction is likely to occur on the cheeks
around the mouth with age.
Finally, regarding the diagonal high-frequency components
shown in the last row of Figure 17, the 1st and 2nd principal component vectors showed the tendency to change the luminance on thecheeks around the mouth, resulting in a correlation coefficient of
0.1195 and 0.6331, respectively. Therefore, from a result of the 2nd
principal component score of the diagonal high-frequency component, there is a correlation between the actual ages and the change inluminance on the cheek, especially around the mouth, and it can beconsidered that a change in luminance tends to occur in that areas.

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Bull. Soc. Photogr. Imag. Japan. Vol. 29 No. 2 (2019)

4. Discussion
In the above section, the principal component analysis was performed, and feature value distributions with significant individual
differences were obtained for frequency components in each direction. By analyzing the relationship between age and principal component score, the trend of changes on the wrinkles and fine asperities in the skin with aging were identified. The tendencies ofchanges over the years are:Low-frequency component: The luminance tends to change especially around the nose.
Horizontal high-frequency component: The luminance tends tochange especially on the forehead.
Vertical high-frequency component: The luminance tends tochange especially on the cheeks and around the mouth.
Diagonal high-frequency component: The luminance tends tochange especially on the cheeks and around the mouth.
The above results are discussed subjectively using actual images ofdifferent ages shown in Figure 18. Figure 18 (a) shows the facial
image of a 25-year-old and Figure 18 (b) shows an 87-year-old.
First, in Figure 18, the older face has deeper wrinkles in the nosethan the younger face. This corresponds reasonably with the result of
the low-frequency component, which is that the luminance depending on the facial gloss is likely to change at both sides of the nose,and the change is larger in the elderly than in the young.
Second, in Figure 18, the older face has slightly deeper wrinkles
on the forehead than the younger face. Since the 2nd principal component score of the subject in the horizontal high- frequency shownin Figure 17 is below the regression line, the facial image shown in
Figure 18 (b) has fewer forehead wrinkles for the age. In otherwords, there is a tendency that a deep wrinkle is seen on the forehead if the weight of the 2nd principle component in horizontal
high frequency has high.
Third, in Figure 18, the older face has deeper wrinkles around themouth than the younger face. These wrinkles in the vertical direction, located on the bottom edge of the mouth, are called the marionette line 6).
Lastly, in Figure 18, the older face has deeper wrinkles on thecheeks and around the mouth than the younger face. These wrinkles
in a diagonal direction, located on the edge of the mouth, are calleda nasolabial fold 6).

In summary, by applying PCA to the components of noticeable
wrinkles and fine asperities with corrected surface reflection components, an index representing the distribution of wrinkles on the faceis acquired. Furthermore, the relationship between the actual age
and its tendency was identified.

5. Conclusion
In this research, we constructed a facial image database of faces of
60 people and determined the component of wrinkles and fine asperities in which the illumination difference of the surface reflection
component was corrected using the proposed method. The acquiredcomponents were subject to a multi-resolution analysis in the same
way as in previous research, and feature values were obtained by resolving the frequency of the skin's wrinkles and fine asperities in thehorizontal, vertical, and diagonal directions. Application of PCA toeach frequency component and analysis of the relationship between
the principal component score and actual age resulted in scores that
identify the lateral and forehead wrinkles of the nasal muscles, themarionette line, and nasolabial fold with age. Moreover, the proposed method enabled us to compare different people or trackchanges of the same person by comparing principal componentscores.
Reconstruction of surface reflection components is needed by aninverse transformation of multi- resolution analysis with modulated
principal component scores. Moreover, we plan to simulate thechange of wrinkles by combining it with the internal reflection component that represents skin color. Additionally, if we can detect lines
from the image in which the proposed method was applied, as
shown in Figure 10, the method is expected to efficiently analyze
wrinkles without multi-resolution analysis and PCA. As a result, theproposed method produces an appropriate analysis of surface reflection components on faces.

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Figure 18 Th e facial image of (a) a young woman (25 years old) and (b)older woman (87 years old).

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