BeautyNet: Joint Multiscale CNN and Transfer Learning Method for Unconstrained Facial Beauty Prediction

Computational Intelligence and Neuroscience
Yikui ZhaiJunying Zeng

Abstract

Because of the lack of discriminative face representations and scarcity of labeled training data, facial beauty prediction (FBP), which aims at assessing facial attractiveness automatically, has become a challenging pattern recognition problem. Inspired by recent promising work on fine-grained image classification using the multiscale architecture to extend the diversity of deep features, BeautyNet for unconstrained facial beauty prediction is proposed in this paper. Firstly, a multiscale network is adopted to improve the discriminative of face features. Secondly, to alleviate the computational burden of the multiscale architecture, MFM (max-feature-map) is utilized as an activation function which can not only lighten the network and speed network convergence but also benefit the performance. Finally, transfer learning strategy is introduced here to mitigate the overfitting phenomenon which is caused by the scarcity of labeled facial beauty samples and improves the proposed BeautyNet's performance. Extensive experiments performed on LSFBD demonstrate that the proposed scheme outperforms the state-of-the-art methods, which can achieve 67.48% classification accuracy.

References

Dec 15, 2015·IEEE Transactions on Pattern Analysis and Machine Intelligence·Ross GirshickJitendra Malik
Jun 1, 2016·IEEE Transactions on Pattern Analysis and Machine Intelligence·Evan ShelhamerTrevor Darrell

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Software Mentioned

Caffe
GoogLeNet
Windows
LightenNet
FaceNet
LighenNet
CNN
DeepFace
Imagenet
BeautyNet

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