# Skin Aging Estimation Scheme Based on Lifestyle and Dermoscopy Image Analysis
## 3 Result and Discussion
### Correlation Analysis between Skin Texture and Life Pattern Changes  
Tables 2-5 show the Pearson correlation coefficients between the skin texture features and life activity of each of the four subjects. For skin texture aging coefficient analysis, we traced the lifestyle of each subject. We collected the life activity data using a questionnaire form that includes BMR questions, amount of sleeping time, eating meals, sun exposure times, drinking, and smoking. The investigated activity categories were studied to provide significant changes in skin condition changes [12; 27; 30]. During eight weeks, we observed daily the subject's skin images and life activity changes. Table 2 presents Pearson correlation analysis results from subject 1. Among the Pearson correlation results, sun exposure time and amount of drinking are significantly correlated with wrinkle length, wrinkle width, and sleeping time changes. In addition, sleeping time has a negative correlation with texture width changes, and drinking has a positive correlation with cell area changes. In summary, in the case of subject 1, the accumulated lack of sleeping time, the amount of sun exposure time, and drinking increased the wrinkle width and decrease the wrinkle length. Table 3 presents Pearson correlation analysis results from subject 2, according to which correlation of sleeping time and skin texture features had the most positive correlation coefficient as compared to other correlation results. We observed a positive correlation of average wrinkle length and total number of cell features. On the contrary, the amount of drinking had an inverse correlation with wrinkle length and the average total number of cells. Subject 2's significant correlation is detected in drinking activities. Table 4 presents Pearson correlation analysis results for subject 3. The cell area, wrinkle length, and total number of cells had a significant correlation with sleeping time. Especially, subject 3's feature changes were highly correlated with sleeping time. Lack of sleeping lead to decreased wrinkle width and cell area. The changes in BMR were correlated with average wrinkle width and length. We considered that these skin features are affected by BMR. Table 5 indicates Pearson correlation analysis results for subject 4. Subject 4's average wrinkle width, cell gradient, and total number of cells were significantly correlated with the accumulated sun exposure time. Especially, average wrinkle width and cell gradient had a positive correlation with accumulated sun exposure time. On the contrary, the average wrinkle length was positively correlated with sleeping time and the total number of cells was negatively correlated with the accumulated amount of drinking. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Life Activity** & **Avg. Wrinkle** & **Avg. Length of** & **Avg. Cell** & **Avg. Cell Gradient** & **Total No. of** \\  & **Width (px)** & **Wrinkle (px)** & **Area (px)** & **(Degrees)** & **Cell (\# Cells)** \\ \hline Sleeping time & \(-0.282\) & \(0.197\) & \(-0.182\) & \(-0.101\) & \(0.182\) \\ BMR & \(0.132\) & \(0.135\) & \(0.152\) & \(0.087\) & \(0.105\) \\ Sun exposure & \(0.217\) & \(-0.242\) & \(0.174\) & \(0.052\) & \(-0.188\) \\ time & \(0.321\) & \(-0.281\) & \(0.284\) & \(0.131\) & \(-0.211\) \\ Amount of & \(0.138\) & \(-0.116\) & \(0.102\) & \(0.125\) & \(-0.123\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Pearson correlation coefficient (subject 1).