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篇名
Good Portrait Selection Based on Deep Learning Using Facial Expressions
並列篇名
Good Portrait Selection Based on Deep Learning Using Facial Expressions
作者 Umer WaqasTae-Young Choe
中文摘要
This paper suggests an algorithm to choose good pictures among a bundle of pictures. Since it is very hard to collect private selections of good images from people, the paper divides facial expressions into ten categories, and add ‘good’ tags to photos included in some categories like wink or grin. The proposed algorithm uses convolution neural networks (CNN) to classify the pictures as good or not. The experimental results show that the accuracy of the algorithm is 97.15%, and average execution time is 0.3 seconds. For application purposes, the proposed algorithm is further applied to a group photo in order to count the number of faces that look good.
起訖頁 132-139
關鍵詞 convolution neural networksdeep learningfacial expressionsimage processingphoto psychologypicture selection
刊名 電腦學刊  
期數 201812 (29:6期)
該期刊-上一篇 BIM Model Components Retrieval Method Based on Visual Attention
該期刊-下一篇 Efficient Least Squares Regression Algorithm for Autonomous Maneuvering UAV System
 

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