# Skin Aging Estimation Scheme Based on Lifestyle and Dermoscopy Image Analysis
## 2 Methods
### Skin Aging Simulation  
Based on the skin aging trend estimation results, we simulated the skin texture aging as shown in Figure 3. First, we extracted skin texture features from Section 2.3. Through the estimation models of each skin feature obtained through Section 2.4, we obtained the skin feature value at the desired age and set it as the simulation target value. After the target values were determined, we separated the wrinkle and cell regions to simulate the aging process.  
Figure 4 shows an example of skin cell and wrinkle region clustering. For calculating the color histogram, we applied the K-means algorithm to the L*a*b* color regions of the two separated regions, respectively. We separated the color region using a K value of five and obtained the average and standard deviation of the L*a*b* color corresponding to each cluster. By subtracting the standard deviation value from the average that each cluster can have, we obtained the effect of image darkening. We repeatedly performed until the value of the prediction targets was reached.  
Figure 3: Flowchart of skin aging simulation.  
Figure 5b,c show the translated cell color region and the translated wrinkle color regions, respectively. Figure 5d shows the result of merging the two images. After color translation for the wrinkle and cell regions was performed, we performed eight neighbors (left-top, top, right-top, left-middle, middle, right-middle, left-down, down and right-down) expansion based on the left and right skeleton of the wrinkle.  
Figure 6a,b indicate the examples of wrinkle width expansion. Before expanding the wrinkle width, we checked the valid expansion points using the color histogram comparison. For eight-neighbor wrinkle width expansion, we proposed the wrinkle width expansion algorithm, which is based on clustered results. Algorithm 3 indicates how to expand the wrinkle width using clustered wrinkle regions. After wrinkle width expansion, we performed cell area and vector expansion.  
Figure 7 provides a brief explanation of cell vector expansion. We calculated the cell vector using starting point A and ending point B. In Figure 7a, the black marked pixel indicates the wrinkle skeleton. The light blue, blue, and red pixels represent different cell regions. We define the maximum diameter of a cell region as a cell vector. In addition, the angle between the start point and end point is defined  
Figure 4: Cell and wrinkle regions clustering using L*a*b* color space and K-means (K = 5)—(**a**) cell; (**b**) wrinkle.  
```
Input: wrinkkle regions cluster \(C_{k}\), wrinkle skeleton \(S_{x,y}\) Output: wrinkle width expanded image S for each wrinkle skeleton \(S(x,y)\) adjacent point \(p_{n}(x,y)\) = ExpansionValidPoint(S(\(x,y\))) for each wrinkle region clusters \(C_{k}\) for each adjacent point \(p_{n}(x,y)\), \([L_{n^{*}},\ a_{n^{*}},\ b_{n^{*}}]\) = Hist(\(p_{n}(x,y)\)) \([L_{k^{*}},\ a_{k^{*}},\ b_{k^{*}}]\) = Hist(Norm(\(C_{k}\))) \(\text{\it colordist}_{n,k}=\sqrt{\left(L_{n^{*}}-L_{k^{*}}\right)^{2}+\left(a_{n ^{*}}-a_{k^{*}}\right)^{2}+\left(b_{n^{*}}-\ b_{k^{*}}\right)^{2}}\) \(\text{\it indexmin}=\text{\it Min}(\text{\it colordist}_{n,k})\) \(\text{\it p}_{n}(x,y)\) = Hist(Norm(\(C_{\text{\it indexmin}}\))) \(\text{\it S}(x,y)\) = \(p_{n}(x,y)\) endfor endfor endfor endfor return S
```  
**Algorithm 3**Winkle width expansion  
Following Algorithm 4, we then calculated the cell vectors and expanded cell vectors. If the average of the cell vector length and angle reached the target value, the algorithm stops and overlaps all processed pixels on the image. Figure 8 shows the results of cell vector expansion. The red straight line in Figure 8c,d indicates the cell vectors that pass through the center of gravity of the cell. We performed the cell vector expansion iteratively until reaching the target cell vector's length and angle.  
Figure 8: Cell vector expansion results—(**a**) merged image; (**b**) cell vector expansion; (**c**) cell vector; (**d**) expanded cell vector.