GAN Junying, JIANG Kaiyong, TAN Haiying, HE Guohui. Facial Beauty Prediction Based on Lighted Deep Convolution Neural Network with Feature Extraction Strengthened[J]. Chinese Journal of Electronics, 2020, 29(2): 312-321. doi: 10.1049/cje.2020.01.009
Citation: GAN Junying, JIANG Kaiyong, TAN Haiying, HE Guohui. Facial Beauty Prediction Based on Lighted Deep Convolution Neural Network with Feature Extraction Strengthened[J]. Chinese Journal of Electronics, 2020, 29(2): 312-321. doi: 10.1049/cje.2020.01.009

Facial Beauty Prediction Based on Lighted Deep Convolution Neural Network with Feature Extraction Strengthened

doi: 10.1049/cje.2020.01.009
Funds:  This work is supported by the National Natural Science Foundation of China (No.61771347), and Basic Research and Applied Basic Research Key Project in General Colleges and Universities of Guangdong Province (No.2018KZDXM073).
  • Received Date: 2017-08-31
  • Rev Recd Date: 2019-06-04
  • Publish Date: 2020-03-10
  • Convolutionneural network (CNN) has significantly pushed forward machine vision,which has achieved very significant results in face recognition, image classification and objection detection,and provides a new method for facial beauty prediction(FBP). Although the approach is widely applied in FBP,the research progress in FBP is relatively slow compared with face recognition. The first one is that there is less public database for FBP,and experiments for FBP are tested on small-scale database.The second one is that evaluation of facial beauty is subjective and lack of criterion,and CNN model is hard to train. In view of the problems of FBP, we expand Largescale database of Asian women's face database (LSAFBD) with data augmentation. A lighted deep convolution neural network (LDCNN) for FBP including 5650K parameters is constructed by both Inception model of GoogleNet and Max-Feature-Max activation layer, which can extract multi-scale features of an image,get compacted presentation and reduce parameters. Experiments on LSAFBD show that our LDCNN model has advantages of simple structure,small-scale parameters and is suitable for small embedded devices,with the best classification accuracy of 63.5%,which outperforms the other published CNN models for FBP.
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