2M BeautyNet: Facial Beauty Prediction Based on Multi-Task Transfer Learning

Gan, Junying; Scotti, Fabio; Li, Xiang; Zhai, Yikui; Mai, Chaoyun; He, Guanghui; Zeng, Junying; Bai, Zhenfeng; Labati, Ruggero Donida; Piuri, Vincenzo

doi:10.1109/access.2020.2968837

Cited by 32 publications

(26 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, 29 features are extracted from facial landmarks. Gan et al [30] proposed a deep learning framework for multi-task transfer learning, which focuses on beauty score prediction as the primary task, treating gender recognition as an auxiliary task. This multi-task framework improves the performance of both tasks and alleviates the over-fitting problem in training the network.…”

Section: Related Workmentioning

confidence: 99%

Assessing Facial Symmetry and Attractiveness using Augmented Reality

Wei

McCay

et al. 2021

Pattern Anal Applic

View full text Add to dashboard Cite

Facial symmetry is a key component in quantifying the perception of beauty. In this paper, we propose a set of facial features computed from facial landmarks which can be extracted at a low computational cost. We quantitatively evaluated the proposed features for predicting perceived attractiveness from human portraits on four benchmark datasets (SCUT-FBP, SCUT-FBP5500, FACES and Chicago Face Database). Experimental results showed that the performance of the proposed features is comparable to those extracted from a set with much denser facial landmarks. The computation of facial features was also implemented as an augmented reality (AR) app developed on Android OS. The app overlays four types of measurements and guidelines over a live video stream, while the facial measurements are computed from the tracked facial landmarks at run time. The developed app can be used to assist plastic surgeons in assessing facial symmetry when planning reconstructive facial surgeries.

show abstract

Section: Related Workmentioning

confidence: 99%

Assessing Facial Symmetry and Attractiveness using Augmented Reality

Wei

McCay

et al. 2021

Pattern Anal Applic

View full text Add to dashboard Cite

show abstract

“…A multi-input multi-task beauty network can also be called 2M BeautyNet. It was used by Gan et al [47] to predict face beauty using transfer learning. The gender recognition step was called the auxiliary, while beauty prediction was known to be the main task.…”

Section: Bfp and Transfer Learningmentioning

confidence: 99%

Facial Beauty Prediction and Analysis based on Deep Convolutional Neural Network: A Review

Saeed

Abdulazeez

2021

JSCDM

View full text Add to dashboard Cite

Facial attractiveness or facial beauty prediction (FBP) is a current study that has several potential usages. It is a key difficulty area in the computer vision domain because of the few public databases related to FBP and its experimental trials on the minor-scale database. Moreover, the evaluation of facial beauty is personalized in nature, with people having personalized favor of beauty. Deep learning techniques have displayed a significant ability in terms of analysis and feature representation. The previous studies focussed on scattered portions of facial beauty with fewer comparisons between diverse techniques. Thus, this article reviewed the recent research on computer prediction and analysis of face beauty based on deep convolution neural network DCNN. Furthermore, the provided possible lines of research and challenges in this article can help researchers in advancing the state – of- art in future work.

show abstract

“…It possessed better real-time performance and accuracy in mainstream CNN models. After that, Gan et al [24] adopted a network named Multi-input Multi-task Beauty Network (2M BeautyNet) and use transfer learning to predict facial beauty, which replace the SoftMax classifier with a random forest achieving better results than before.…”

Section: A Facial Beauty Predictionmentioning

confidence: 99%

“…We use Pearson correlation (PC) [45] to evaluate our method for FBP. The experimental results of deep learning method in the database SCUT-FBP5500 in Table 10 are from paper [24]. Experimental results show that our method can still achieve good results similar to deep learning in a very short time.…”

Section: B Experiments On Local Feature Fusion and Blsmentioning

confidence: 99%

See 1 more Smart Citation

Facial Beauty Prediction via Local Feature Fusion and Broad Learning System

Zhai

Qin

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

Facial beauty prediction (FBP), as a frontier topic in the domain of artificial intelligence regarding anthropology, has witnessed some good results as deep learning technology progressively develops. However, it is still limited by the complexity of the deep structure network in need of a large number of parameters and high dimensions, easily leading to a great consumption of time. To solve this problem, this paper proposes a fast training FBP method based on local feature fusion and broad learning system (BLS). Firstly, two-dimensional principal component analysis (2DPCA) is employed to reduce the dimension of the local texture image so as to lessen its redundancy. Secondly, local feature fusion method is adopted to extract more advanced features through avoiding the effects from unstable illumination, individual differences, and various postures. Finally, extensional feature eigenvectors are input to the broad learning network to train an efficient FBP model, which effectively shortens operational time and improve its preciseness. Extensive experiments with the proposed method on large scale Asian female beauty database (LSAFBD) can be conducted within 13.33s while sustaining an accuracy of 58.97%, impressively outstripping other state-of-the-art methods in training speed. INDEX TERMS Facial beauty prediction (FBP); local feature fusion; broad learning system (BLS);

show abstract

2M BeautyNet: Facial Beauty Prediction Based on Multi-Task Transfer Learning

Cited by 32 publications

References 45 publications

Assessing Facial Symmetry and Attractiveness using Augmented Reality

Assessing Facial Symmetry and Attractiveness using Augmented Reality

Facial Beauty Prediction and Analysis based on Deep Convolutional Neural Network: A Review

Facial Beauty Prediction via Local Feature Fusion and Broad Learning System

Contact Info

Product

Resources

About