S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
Award Criteria
Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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Integrated Image Sensor and Hyperparameter Optimization of Convolutional Neural Network for Facial Skin Detection

Hsueh-Yi Lin, Cheng-Jian Lin, Shiou-Yun Jeng, and Cheng-Yi Yu

(Received January 20, 2021; Accepted May 10, 2021)

Keywords: skin detection, convolutional neural network, cosmetology, Taguchi method, orthogonal array, hyperparameter optimization

Detection of the facial skin condition enables people to better understand skin problems and helps them select appropriate treatment methods and maintenance products. We collected facial images of different skin problems by using an image optical sensor. To overcome the problem of insufficient training data, the collected dataset was expanded through data augmentation. In the field of computer vision, deep learning is often used for solving image recognition problems with high accuracy. Therefore, we used a convolutional neural network (CNN) to detect facial images of different skin problems. To overcome the difficulty of parameter selection and increase the recognition rate and robustness of the CNN, the parameters of the CNN were optimized using the Taguchi method. Eight control factors in the convolutional layer and the L36 orthogonal array were used in experiments. Analysis of variance was used for statistical analysis in the design of the experiments to obtain the optimal parameter combination for the developed CNNs. The experimental results indicate that the CNN optimized using the Taguchi method had an accuracy of 86.95%. The accuracy of the optimized CNN was 7.24% higher than that of the original CNN. The experimental results prove that the proposed hyperparameter optimization method can effectively improve the accuracy of network detection.

Corresponding author: Cheng-Jian Lin




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