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

GAN Junying; JIANG Kaiyong; TAN Haiying; HE Guohui

doi:10.1049/cje.2020.01.009

Volume 29 Issue 2

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GAN Junying, JIANG Kaiyong, TAN Haiying, et al., “Facial Beauty Prediction Based on Lighted Deep Convolution Neural Network with Feature Extraction Strengthened,” Chinese Journal of Electronics, vol. 29, no. 2, pp. 312-321, 2020, doi: 10.1049/cje.2020.01.009

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GAN Junying, JIANG Kaiyong, TAN Haiying, et al., “Facial Beauty Prediction Based on Lighted Deep Convolution Neural Network with Feature Extraction Strengthened,” Chinese Journal of Electronics, vol. 29, no. 2, pp. 312-321, 2020, doi: 10.1049/cje.2020.01.009

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

doi: 10.1049/cje.2020.01.009

School of Intelligent Manufacturin, Wuyi University, Jiangmen 529020, China

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

Abstract

Abstract

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.
- Lighted deep convolution neural network,
- Facial beauty prediction,
- Inception model,
- MFM activation layer,
- LSAFBD

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References(21)

References

Krizhevsky A., Sutskever I., and Hinton G. “Imagenet classification with deep convolutional neural networks”, Advances in neural information processing systems, Vol.25, No.2, pp.1097-1105, 2012.

Zeiler M. D. and Fergus R. “Visualizing and understanding convolutional neural networks”, European Conference on Computer Vision, Zurich, Switzerland, pp.818-833, 2014.

Sun Y., Wang X. and Tang X. “Deep Learning Face Representation from Predicting 10,000 Classes”, 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, pp.1891-1898, 2014.

Simonyan K. and Zisserman A. “Very deep convolutional networks for large-scale image recognition”, Computer Science, Vol.1, No.1, pp.1-14, 2014.

Szegedy C., Liu W., Jia Y., et al., “Going Deeper with Convolutions”, 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, pp.1-9, 2015.

He K., Zhang X., Ren S., et al., “Deep residual learning for image recognition”, 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, Vol.1, pp.770-778, 2016.

Ren S., He K., Girshick R., et al., “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Montreal, Canada, Vol.39, No.6, pp.1137-1149, 2015.

Sun Y., Chen Y., Wang X., et al., “Deep learning face representation by joint identification verification”, The 28 Annual Conference on Neural Information Processing Systems, pp.1988-1996, 2014.

He K., Zhang X., Ren S., et al., “Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification”, 2015 IEEE International Conference on Computer Vision, Santiago, Chile, pp.1026-1034, 2015.

Huang G.B., Ramesh M., Berg T., et al., “Labeled faces in the wild: A database for studying face recognition in unconstrained environments”, Advances in Face Detection and Facial Image Analysis, pp.189-248,2008.

Liu W., Anguelov D., Erhan D., et al., “SSD: Single Shot MultiBox Detector”, The 14th European Conference on Computer Vision, Amsterdam, The Netherlands, pp.21—37, 2016.

Gray D., Yu K., Xu W., et al., “Predicting Facial Beauty without Landmark”, European Conference on Computer Vision, Heraklion, Crete, Greece, Vol.6316, No.4, pp.434-447, 2010.

Xie D., Liang L., Jin L. W., et al., “SCUT-FBP: a benchmark dataset for facial beauty perception”, International Conference on Systems, Man, and Cybernetics, Kowloon, China, pp.1821-1826, 2015.

Xu J, Jin L W, Liang L, et al., “A new humanlike facial attractiveness predictor with cascaded fine-tuning deep learning model”, Computer Science, Vol.70, No.1, pp.45-79, 2015.

Xu J., Jin L., Liang L., et al., “Facial attractiveness prediction using psychologically inspired convolutional neural network (PI-CNN)”, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing, New Orleans, LA, USA, pp.335-339, 2017.

Taigman Y., Yang M., Marc, et al., “DeepFace: Closing the Gap to Human-Level Performance in Face Verification”, 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, pp.1701-1708, 2014.

Wu X, He R, Sun Z, et al., “A Light CNN for Deep Face Representation with Noisy Labels”, IEEE Transactions on Information Forensics and Security, Vol.13, No.11, pp.2884-2896, 2018.

Goodfellow I. J., Warde-Farley D., Mirza M., et al., “Maxout Networks”, Computer Science, Vol.28, No.3, pp.1319-1327, 2013.

Gan J. Y., Zhai Y. K. and Wang B., “Unconstrained facial beauty prediction based on multi-scale k-means”, Chinese Journal of Electronics, Vol.26, No.3, pp.548-556, 2017.

Sun Y., Chen Y., Wang X., et al., “Deep convolutional network cascade for facial point detection”, 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, pp.3476-3483, 2013.

Lin M., Chen Q. and Yan S., “Network in Network”, Computer Science, Vol.1, No.1, pp.85-97, 2013.

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