Automatic Facial Attractiveness Prediction by Deep Multi-Task Learning

Gaol, Lian; Li, Weixin; Huang, Z. J.; Huang, Di; Wang, Yunlong

doi:10.1109/icpr.2018.8545033

Cited by 19 publications

(17 citation statements)

References 17 publications

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“…Additionally, we demonstrate that using our model to select the best dating photos is as accurate as having 10 humans vote on each photo and selecting the best average score. Architecture SCUT-FBP Best Run SCUT-FBP 5 Fold CV HotOrNot MLP [18] 76 71 -AlexNet-1 [42] 90 84 -AlexNet-2 [42] 92 88 -PI-CNN [18] 87 86 -CF [17] 88 --LDL [41] 93 --DRL [25] 93 --MT-CNN [42] 92 90 -CR-Net [22] 87 -48. Through this work, we also conclude that Photofeeler's normalizing and weighting algorithm dramatically decreases noise in the votes.…”

Section: Resultsmentioning

confidence: 99%

Photofeeler-D3: A Neural Network with Voter Modeling for Dating Photo Impression Prediction

Kalra¹,

Peterson²

2019

Advances in Intelligent Systems and Computing

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In just a few years, online dating has become the dominant way that young people meet to date, making the deceptively error-prone task of picking good dating profile photos vital to a generation's ability to form romantic connections. Until now, artificial intelligence approaches to Dating Photo Impression Prediction (DPIP) have been very inaccurate, unadaptable to real-world application, and have only taken into account a subject's physical attractiveness. To that effect, we propose Photofeeler-D3 -the first convolutional neural network as accurate as 10 human votes for how smart, trustworthy, and attractive the subject appears in highly variable dating photos. Our "attractive" output is also applicable to Facial Beauty Prediction (FBP), making Photofeeler-D3 state-of-the-art for both DPIP and FBP. We achieve this by leveraging Photofeeler's Dating Dataset (PDD) with over 1 million images and tens of millions of votes, our novel technique of voter modeling, and cutting-edge computer vision techniques.

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Section: Resultsmentioning

confidence: 99%

Photofeeler-D3: A Neural Network with Voter Modeling for Dating Photo Impression Prediction

Kalra¹,

Peterson²

2019

Advances in Intelligent Systems and Computing

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“…The conventional techniques are immensely handcrafted attribute-based [16], [30], [31], [32]; their attributes are structured based on limited evidenced and common cognition regulations associated with facial attractiveness based on both shape and texture [33], [34], [35]. Because the modeling of beauty is difficult using traditional techniques, this qualifies it as an adequate subject for data-driven techniques like deep learning [36].…”

Section: Fbp and Deep Learning Techniquesmentioning

confidence: 99%

“…It has been established by Gao et al [33] that deep multi-task learning techniques were used to solve automatic FAP problems. A study proposed a combined CNN model that takes geometric characteristics and appearance into consideration on a simultaneous benchmark SCUTFBP.…”

Section: Fig 3 -Geometric Landmark Representation Example [24]mentioning

confidence: 99%

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

Saeed

Abdulazeez

2021

JSCDM

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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.

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“…Xie et al [41] develop a face dataset with attractiveness ratings (SCUT-FBP) for automatic facial beauty perception. This benchmark dataset intrigues further deep learning based prediction methods, such as the psychology-inspired CNN-based method (PI-CNN) [42], self-taught learning [14], label distribution learning (LDL) [13], feature combination [9], and multitask learning [15]. Rothe et al [33] rate a specific person from a given portrait image based on the dataset on ℎ ℎ .…”

Section: Related Workmentioning

confidence: 99%