Journal of Cosmetics, Dermatological Sciences and Applications, 2013, 3, 28-35 Published Online January 2013 (
Automatic Facial Spots and Acnes Detection System
Chuan-Yu Chang, Heng-Yi Liao
Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin,
Received November 13th, 2012; revised December 16th, 2012; accepted December 25th, 2012
Recently medical cosmetic has attracted significant business opportunity. Micro cosmetic surgery usually involves in-
vasive cosmetic procedures such as non-ablative laser procedure for skin rejuvenation. However, to select an appropri-
ate treatment for skin relies on accurate preoperative evaluations. In this paper, an automatic facial skin defects detec-
tion and recognition method is proposed. The system first locates the facial region from the input image. Then, the
shapes of faces were recognized using a contour descriptor. The facial features are extracted to define regions of interest
and an image segment method is used to extract potential defect. A support-vector-machine-based classifier is then used
to classify the potential defects into spots, acnes and normal skin. Experimental results demonstrate effectiveness of the
proposed method.
Keywords: Medical Image Analysis; Texture Recognition; Skin Disease Identification; Spot and Acne Detection
1. Introduction
Recently medical cosmetology has a great development.
It usually involves invasive cosmetic procedures or op-
eration such as non-ablative laser procedure for skin re-
juvenation and filling injection to relieve wrinkles [1,2].
Most conventional skin analysis instruments are contact-
based. A physician has to visually inspect the target re-
gion and applies a contact test probe to magnify the tar-
get region for inspection. This contact testing procedure
may be unsanitary. Non-contact inspections analyze pa-
tients’ facial skin conditions by a camera directly. Doc-
tors and patients are not necessary to be face to face.
Furthermore, remote defect detection for consultation
before cosmetology is possible.
VISIA is a widely used commercial instrument for
skin analysis in cosmetic surgery. The skin condition is
analyzed by multispectral images [3]. However, the cost
is high and physicians should manual outline regions of
interest (ROIs) during inspection.
With gradually higher resolution in digital cameras,
many digital imaging methods have been proposed to
analyze skin conditions [4-7]. These investigations ap-
plied various color quantization methods to distinguish
whether the ROI is a spot or not. However, using pure
color information to detect spots is difficult because
shadow of facial organs (eyes, nose, mouth, or ears) may
be misjudged as spots.
In addition, most skin analysis systems require manu-
ally outlining the regions of interest (ROIs) [3-5]. How-
ever, manual ROI outlining is commonly known as a
time-consuming and non-repeatable process.
In our prior work, an automatic facial skin defect de-
tection system was proposed [7]. However, the facial
view was restricted to front. Hence, a novel skin condi-
tions evaluation system, which integrates a multi-view
image acquisition device and automatic facial skin defect
detection, is proposed in this paper. A facial features de-
tection approach is applied to obtain positions of the fa-
cial features which are further applied to extract the ROI.
A special color space and an adaptive threshold based on
probability distribution are used to characterize potential
defects from the ROI. Finally some significant texture
features are extracted and applied to a specific designed
classifier to classify potential defects into normal skin,
spots and acnes.
The rest of this paper is organized as follows. Section
2 introduces details of the proposed skin defects detec-
tion and recognition system including definition of ROI,
potential defect extraction and texture features used for
classification. Section 3 demonstrates the experimental
result. Conclusions and possible research topics in the
future are given in Section 4.
2. The Proposed System
In this paper, an automatic facial skin defects detection
and recognition system is proposed. When the facial im-
ages are captured by a high definition camera, the skin
defects are detected and recognized automatically. Fig-
Copyright © 2013 SciRes. JCDSA
Automatic Facial Spots and Acnes Detection System 29
ure 1 illustrates the processes of the proposed approach,
includes face region detection, facial view detection,
front/profile ROI extraction, potential defect extraction
and classification. Details of these processes are described
in the following subsections.
2.1. Facial Region Detection
A skin color detection method is applied to detect facial
region. Soriano [8] proposed a skin locus in normalized
color coordinate (NCC). This skin color model has per-
formed well with images under widely varying condi-
tions [9]. Therefore, the skin locus is adopted to detect
skin-like region in this paper. A region filling method is
applied to the largest connected region which is obtained
by an 8-adjacent connected component labeling algo-
rithm. The largest skin region is regarded as the facial
region [10]. The facial region detection result is shown in
the Figure 2.
2.2. Facial View Detection
Before extraction of the ROI, the facial view needs to be
determined. The shape of face can be used to distinguish
between front and profile. Fourier descriptors have been
widely used for representing boundary of a two-dimen-
sional shape and it has the advantages of affine-invariant
such as translation, rotation, and scaling [10]. In these
descriptors, the centroid distance is used to represent the
shape. The centroid distance Cd(t) is defined as follow:
Figure 1. The flowchart of the proposed system.
(a) (b)
Figure 2. The facial region detection result: (a) Original
image; (b) Detection result.
, 0,1,,1
tc tc
Cd txxyytN
  (1)
xy y
where (xt, yt) is the t-th point on the face contour, (xc, yc)
is the centroid of the face contour, and N is the number of
sampling points. Fourier transform of Cd(t) is defined as
()exp , 0,1,,1
FDCd tkN
Since Cd(t) is a real value, there are only N/2 different
frequencies in the Fourier transform, only half of FDk is
needed to represent the face contour. Scale invariance is
then obtained by dividing the magnitude values of the
first half of FDk by DC component. The feature vector F
is used as the input vector of support vector machine for
training and testing.
00 0
F (4)
Because the Fourier descriptors have the attribution of
affine-invariant, left and right profiles have the same
result from the classifier. Hence, the direction of profile
needs to be detected. The skin color distribution is used
to distinguish between left and right profile. The numbers
of skin pixels in the left profile and right profile are cal-
culated respectively.
Figure 3 shows the statistics of skin pixels in the left
profile and right profile, where R and L represent the
number of skin pixel in the right profile and the left one,
respectively. If L is large than R, the direction of face is
right, otherwise is left.
2.3. ROI Extraction
The facial features are further adopted to exclude unde-
sired regions, including eyes, eyebrows, mouth and nos-
trils. The facial features are detected beforehand using
empiricals information in YCbCr and HSV color spaces
[7,11]. First, the pupils are detected under the YCbCr
color space in the rough regions of both eyes. The rough
Copyright © 2013 SciRes. JCDSA
Automatic Facial Spots and Acnes Detection System
(a) (b)
Figure 3. (a) Right profile; (b) Left profile; (c) The number
of skin pixel in the right profile; (d) The number of skin
pixel in the left profile.
regions of other facial features are located according to
the position of pupils, as shown in the Figure 4. The eyes,
eyebrows, mouth and nostrils are extracted by the Sobel,
HSV color space, YCbCr color space and difference of
Gaussian respectively in the corresponding region. The
ROI is obtained by removing the facial features from the
facial region. Figure 5 shows the results of ROI extrac-
tion in different view.
2.4. Potential Defect Extraction
In order to extract potential defects from the ROI, a seg-
mentation method for potential defect detection is pro-
posed. Because the surface of face is not a flat surface,
(a) (b)
Figure 4. The rough regions of facial features: (a) Front; (b)
Figure 5. The results of ROI extraction.
skin color may varied in different positions on the same
objects, even though the area focuses only on the fore-
head [7]. Therefore, the ROI image is divided into n non-
overlap sub-images regularly. In this paper, the width
and height of every sub-image are set to 100.
The selection of color space is important for pattern
recognition. Cr in YCbCr and AngleA has well human
visual feedback for acnes and spots, respectively [12].
Therefore, Cr -AngleA color space is adopted to detect
potential defects. The transformation functions are de-
fined as follows:
0.50.4190.08 128Cr RGB
 (5)
Anglecos B
and R, G, B are three values corresponding to red, green
and blue component in the original RGB color space. The
range of Cr value is [0, 255] and AngleA is [0, 90].
Figure 6 shows an example of skin and defects distri-
bution in Cr-AngleA space between two sub-images. We
can find that the spot and acne have relatively high val-
ues in AngleA and Cr, respectively.
The center of skin color distribution (Cx, Cy) is ob-
tained by the maximum count of histogram in Cr and
AngleA respectively. Cx and Cy are defined as follows:
arg maxkCr
CxH b (8)
arg maxk
CyH b (9)
where Hk() is the histogram in the k level. The distance
transform D(x, y) between observational value and the
center is defined as follow:
Copyright © 2013 SciRes. JCDSA
Automatic Facial Spots and Acnes Detection System 31
Figure 6. The example of skin and defects distribution in
Cr-AngleA space.
 
,, ,
Cr A
Dxybxy Cxbxy Cy
 
If the distance transform is calculated, a threshold
needs to be determined for distinguish potential defects
and normal patterns. Then, the histogram thresholding
based method is proposed. According to the observation,
the probability density function of defect pixels occur-
rence in a sub-image is approximated by a Poisson dis-
tribution. Let the distances of D(x, y) be represented in L
levels [0, 1, , L]. The number of distances at level k is
denoted by nk and the total number of distances is N = n1
+ n2 +  + nL. The histogram of D(x, y) is regarded as a
probability density function pk:
,0, and 1
kk kk
pnN pp
The curve fitting function fk(λ) which is shift by the
Poisson is defined as follows:
 (12)
arg maxk
Then, fk(λ) is scale to match pk, Equation (12) is modi-
fied to Equation (14) which is defined as:
 
ff f
 (14)
where λ = 0, 1, , L. The fittest curve p*k is defined by
finding a λ such that the difference between pk and f'k(λ)
is minimum, and it is defined as follows:
*argmin ()
The threshold t is set to the maximum value of the
second order derivatives of p*k. In other words, the pixel
is regarded as foreground if the distance between obser-
vational pixel and the center in Cr-AngleA color space is
larger than t; otherwise it is regarded as background. The
threshold t is defined as Equation (16).
arg max*k
where () is the second order derivative. The fittest curve
and its second order derivative are shown in the Figure
The potential defect is denoted by S(x, y)
 
1if ,
Dxy t
Sxy 
Figure 8 shows the potential defect extraction method
step by step. The potential defects can be obtained by a
4-adjacent connected component labeling algorithm.
2.5. Classification
2.5.1. Feature Extraction
To further classify the potential defects into normal pat-
terns, acnes, and spots, the texture features calculated
from co-occurrence matrix are used. The co-occurrence
matrix is introduced by Haralick [13], which indicates
probability of grey-level i occurring in the neighborhood
of grey-level j at a distance d and direction
. Fourteen
co-occurrence matrix features including Contrast, Ho-
mogeneity, Mean, Variance, Energy, Entropy, Angular
Second Mom ent , Correlation, Sum Average, Sum En-
tropy, Difference Average, Difference Variance and Dif-
ference Entropy are extracted [14]. In this paper, the dis-
tance was chosen as one pixel and four angles (0˚, 45˚,
90˚, 135˚) were selected.
To preserve spatial details, the texture features of the
four directions are averaged. In addition to gray color,
the color space Cr and AngleA are also adopted. Ac-
cordingly, there are 14 × 3 GLCM features calculated
from each potential defect. The averaged GLCM feature
in the color space C is denoted as
,0 ,45 ,90 ,135
tt t t
where ,
represents the feature f with angle
in color
C. A geometric feature roundness defined as Equation
(17) is also adopted.
roundness 4π
where E and A are the perimeter and the area in a poten-
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Automatic Facial Spots and Acnes Detection System
Copyright © 2013 SciRes. JCDSA
Figure 7. The result of threshold detection in a sub-image.
Figure 8. The potential defects extraction processing.
tial defect, respectively. Therefore, there are 42 + 1 fea-
tures in candidate features.
2.5.2. Feature Selection
In order to sift the most significant features, the sequen-
tial floating forward selection (SFFS) is adopted for fea-
ture selection [15]. This method consists of applying se-
quential forward selection (SFS) and sequential back-
ward selection (SBS). By using the SFS and SBS re-
peatedly, it will converge when the defect recognition
rate does not increase. In this paper, the cross validation
rate is defined as the defect recognition rate which is
implemented by LIBSVM [16]. There are 126 acnes, 134
spots and 134 normal patterns which are extracted manu-
ally from the database. Figure 9 shows some examples
of the training patterns.
2.5.3. Feature Extraction
Support vector machine (SVM) is a popular and robust
classifier in classification and regression analysis task.
The SVM constructs a hyperplane in a high dimensional
space which has the largest distance to the nearest train-
ing data points of any class. Figure 10 shows the struc-
ture of the classifier. The classifier is utilized to classify
the potential skin defects into normal patterns, acnes and
spots, and the structure of classifier is a decision tree
structure which consists of two SVMs. The SVM1 is
used to classify defects and normal patterns from the
potential defects. The other SVM2 is used to classify
acnes and spots from the defects. Moreover, the SFFS
algorithm is carried out individually in each stage.
3. Experimental Results
3.1. Experiment Environment
To reduce the influence of illumination, a special de-
signed image acquisition device is created. Faces are
captured by a high-resolution camera with resolution of
10 M pixels.
Figure 11 shows the acquisition device. In this device,
the acquisition parameters including camera parameters,
light source, distances from subjects’ face to the camera
are all fixed. The camera is Cannon Power Shot G10.
ISO speed is set to 1/60 sec. Focal length is set to auto-
matic mode. Because the forehead is an important part in
medical cosmetology, every subject is asked to move
Automatic Facial Spots and Acnes Detection System 33
(a) (b) (c)
Figure 9. Some examples of three skin conditions: (a) Acnes;
(b) Spots; (c) Normal patterns.
Figure 10. The structure of classifier.
Figure 11. Image acquisition device.
their hair aside in order to show their forehead. Figure
12 shows some samples captured in this study. The sys-
tem is implemented with Visual C# and Python 2.5 on an
Intel Core 2 Quad 2.66 GHz processor and 2 GB RAM
3.2. Experimental Database
To demonstrate the capability of the proposed method,
there are two face image database YUFH and YUFH2
are used. YUFH was created by Chang [7], and there are
93 face images taken from 3 females and 26 males in 3
views (front, left profile and right profile). YUFH2 is
collected by our image acquisition device. There are 54
face images taken from 3 females and 15 males in 3
3.3. Face View Classification Result
To demonstrate the capability of the face view classifi-
cation, the two databases are used. In each database, a
half of subjects are used for training, rest of subjects are
used for testing. The Confusion Matrix for face view
classification result in the database YUFH and YUFH2
are shown in Tables 1 and 2 respectively.
3.4. Feature Selection Result
The result of feature selection by SFFS for SVM1 is shown
(a) (b) (c)
Figure 12. Some examples in the database: (a) YUFH; (b),
(c) YUFH2.
Table 1. The confusion matrix for face view classification in
Actuality Front Profile Accuracy
Front 12 0 100.0
Profile 0 24 100.0
Average accuracy 100.0
Table 2. The confusion matrix for face view classification in
Actuality Front Profile Accuracy
Front 8 1 88.9
Profile 0 18 100.0
Average accuracy 96.2
in Table 3. For SVM2, the result of feature selection by
SFFS is shown in Table 4.
3.5. Defect Detection Result
To quantify the performance of the proposed approach,
three standardized measurements are adopted: Accuracy,
Sensitivity and Specificity. They are defined respectively
as follows:
Accuracy TP TN
where RP is the total number of defect regions and RN is
the total number of normal regions. RTP is the number of
regions in the actual defect region and is classified as
defect by the proposed system and RTN is the number of
regions in the actual normal region and classified as
normal by the system. In this paper, the RN is defined as
RP subtracted from the total number of potential defect
Table 5 shows the evaluation results of the proposed
approach in the YUFH database. There are 93 face im-
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Automatic Facial Spots and Acnes Detection System
Table 3.The result of SFFS for SVM1.
Feature Color Space
Difference Variance
Sum Variance
Sum Average
Difference Average
Table 4. The result of SFFS for SVM2.
Feature Color Space
Angular Second Moment
Sum Entropy
Difference Average
Difference Variance
Difference Entropy
Angular Second Moment
Difference Average
Difference Variance
Table 5. The evaluation result of the proposed approach in
Accuracy Sensitivity Specificity
Spot 96.94% 76.56% 97.56%
Acne 98.22% 64.52% 98.41%
ages including front and profile. Table 6 shows the evalua-
tion results of the proposed approach in the YUFH2 da-
tabase. There are 54 face images including front and pro-
file. In these tables, the Sensitivity measurement in acne
detection is relatively low than the others. The reason is
that acnes have various types and stages in clinical
Figure 13 shows the result of defects detection, in
which the spots and acnes are outlined in blue and red,
We compared the performance of the proposed ap-
proach for spots with Chang [6] and Chang [7]. Table 7
Table 6. The evaluation result of the proposed approach in
Accuracy Sensitivity Specificity
Spot 93.63% 75.62% 94.59%
Acne 99.68% 69.73% 99.90%
Table 7. Comparison of the proposed approach and others.
Accuracy Sensitivity Specificity
Proposed method99.40% 80.91% 99.42%
Chang [6] 98.76% 54.34% 98.81%
Chang [7] 99.22% 63.24% 99.25%
(a) (b)
Figure 13. (a) Original image; (b) Result of defects detection.
shows the results obtained using the proposed approach
and [6,7]. There are 97 images including 32 males and 4
females from both YUFH and YUFH2. From this table,
one can see accuracies of the proposed approach for
spots detection are higher than those of [6] and [7].
4. Conclusions and Future Directions
Skin analysis is one of the most important procedures
before medical cosmetology. In this paper, an automatic
facial skin defects detection and recognition system is
proposed. Different from other methods which have to
manually outline the ROIs in a face image, the proposed
approach obtains the ROI automatically in both front and
profile views.
The proposed system first locates the facial region
from the input image. The facial features and skin color
were used to locate the ROI. An approximate Poisson
distribution is used to define a suitable threshold for ex-
tracting potential defects. Then, the SFFS is adopted to
select significant features for defects classification. Fi-
nally, a decision tree classifier consists of two SVMs is
applied to classify the potential defects into normal pat-
terns, spots and acnes. Experimental results show that the
proposed method can detect facial skin defect and recog-
nize the lesion effectively.
In the future work, more images of subjects in differ-
ent ages will be collected and tested. The accuracy of the
current system can increase by attempting more features.
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Automatic Facial Spots and Acnes Detection System
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5. Acknowledgements
This work was supported by the National Science Coun-
cil Taiwan, under Grants NSC 98-2220-E-224-02.
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