臺灣博碩士論文加值系統

皮膚的顏色在臨床醫學、皮膚醫學和化妝品科學中，因其容易觀察，間接暗示了個人的健康狀況，甚至影響到外觀具吸引力與否而成為被廣泛研究的議題。然而，受肉眼和主觀因素的影響，皮膚的細微顏色變化例如局部血流變化或黑色素量變化不容易單憑肉眼察覺。使用光譜儀量測皮膚的反射係數能夠更精確計算皮膚顏色。然而由於光譜儀極其敏感的性質，使用光譜儀量測皮膚時的量測架設、力道及部位變動，皆可能造成嚴重誤差。再者，皮膚顏色分佈是由複雜的生理構造所形成，且皮膚表面紋理、角質及毛髮都可能造成截然不同的量測結果。如何量測皮膚光譜及何謂具代表性皮膚光譜都是值得深入探討的學問。
有鑒於深度學習近年來在影像識別、語音辨識、高光譜影像分類的效果皆超越傳統的分類器，本論文提出使用一維摺積類神經網路來達成穩健的費式量表(Fitzpatrick scale)皮膚類別分類器，比較(a)X-Rite i1Pro (b)晶片微型光譜儀 所建置的皮膚光譜資料庫 及 (c)他方建置的皮膚光譜資料庫UWA-FSRD 的分類結果。相較於傳統皮膚醫學使用的ITA分類方式得到的分類結果：(a)81.94% (b)83.2% (c)77.89%，使用類神經網路的分類結果：(a)96.88% (b)86.7% (c)81.82%及使用本論文所提出的摺積類神經網路的分類結果：(a)92.59% (b)90.4% (c)84.94%，使用類神經網路及摺積類神經網路可提升大約10%的準確率。整體而言，在訓練樣本充足及均勻的資料庫中，本論文所提出的摺積類神經網路可得到最好的費式量表皮膚類別分辨率。

Skin spectrum is used in a wide range of applications including medical science, dermatology, cosmetics science, and biometric face recognition. However, it is noticed that the composition of complex tissue layers and the uneven outer surface of the skin make skin spectrum evaluation error-prone. In other words, the skin reflection spectra of the same measurement area from the same person could show different spectral characteristic. Since deep neural networks show robust classification in the areas of visual recognition, image labeling, speech recognition, hyperspectral image…etc. A Convolutional Neural Network (CNN) is introduced for studying robust Fitzpatrick scale classification.
Adapting CNN models used in the area of image classification, modified one-dimensional CNN is implemented into skin spectrum classification. The performances of traditional Individual Typology Angle (ITA) approach, Artificial Neural Networks (ANN) and CNN are compared. In our study, three data sets are used to exam our proposed Fitzpatrick scale assessment: (a)a self-built skin spectrum database using X-Rite i1Pro (b)a self-built skin spectrum database using on-chip micro sensor (c)a public facial spectral reflectance database. Comparing to the results of ITA approach which are (a)81.94% (b)83.2% (c)77.89%, ANN achieves the results of (a)96.88% (b)86.7% (c)81.82% and CNN achieves the results of (a)92.59% (b)90.4% (c)84.94%. With the machine learning approaches, the Fitzpatrick skin type classification rate can improve around 10%. Overall, with the sufficient training samples and evenly distributed class numbers, our proposed CNN model is a robust skin type classifier.

摘　要 i
ABSTRACT ii
誌　謝 iii
Contents iv
List of Tables vi
List of Figures vii
Chapter 1 INTRODUCTION 1
1.1 Preface 1
1.2 Motivation 2
1.3 Organization 5
Chapter 2 BACKGROUND KNOWLEDGE 6
2.1 Color space and L*a*b* 6
2.2 Conventional skin color evaluation system and skin classification 9
2.2.1 Fitzpatrick scale 9
2.2.2 Pantone skintone guide 10
2.2.3 The error-prone results of conventional skin classification 11
2.3 Machine learning 14
2.3.1 Artificial neural networks 14
2.3.2 Deep neural networks 24
2.3.3 Convolutional neural networks 24
Chapter 3 THE METHODOLOGY OF SKIN SPECTRUM CLASSIFICATION 31
3.1 Methodology of artificial neural network in spectrum classification 31
3.1.1 The proposed structure of artificial neural network in spectrum classification 31
3.1.2 Training procedures of ANN 33
3.2 Methodology of convolutional neural networks in spectrum classification 34
3.2.1 The proposed structure of convolutional neural networks in spectrum classification 34
3.2.2 Training procedures of CNN 37
3.3 System overview 38
Chapter 4 EXPERIMENTAL RESULTS 39
4.1 Experiment setup 39
4.2 Training data sets 40
4.2.1 Self-build skin database1 42
4.2.2 Self-build skin database2 44
4.2.3 Public skin database – UWA-FSRD 46
4. 3 Experimental results and discussion 50
Chapter 5 CONCLUSION AND FUTURE WORKS 57
5.1 Conclusions 57
5.2 Future Works 58
REFERENCES 59

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