CRNet: Classification and Regression Neural Network for Facial Beauty Prediction

Xu, Lu; Xiang, Jinhai; Yuan, Xiaohui

doi:10.1007/978-3-030-00764-5_61

Cited by 23 publications

(18 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

See 1 more Smart Citation

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

Kalra¹,

Peterson²

2019

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

“…Results are available in Table 5. All other FBP methods [22,23,39,23] first use the Viola-Jones algorithm to crop out the faces and then forward pass their models. Our method takes in the full image, resizes it to 600x600, and forward passes the Photofeeler-D3 network.…”

Section: Photofeeler-d3 In Fbpmentioning

confidence: 99%

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

Kalra¹,

Peterson²

2019

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

show abstract

“…An individual face was labeled using a rating; however, this dataset has been divided into test and training sets. The concluding result was determined using an average of 5-fold [16], [22], [25].…”

Section: Pfb Benchmark Datasetmentioning

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

“…This cat-egory of tasks are rare, where most of them target at "human facial beauty prediction" such as [8] and [11]. L Xu et al proposed a new network framework, Classification and Regression Network (CRNet) [12], which uses different branches to handle classification and regression tasks simultaneously. Adapting rich deep features to facial beauty prediction has been applied as feature extraction techniques reported in [13] and [14].…”

Section: Introductionmentioning

confidence: 99%

PETS-SWINF: A regression method that considers images with metadata based Neural Network for pawpularity prediction on 2021 Kaggle Competition "PetFinder.my"

Wang¹,

Liu²

2022

Preprint

View full text Add to dashboard Cite

Millions of stray animals suffer on the streets or are euthanized in shelters every day around the world. In order to better adopt stray animals, scoring the pawpularity (cuteness) of stray animals is very important, but evaluating the pawpularity of animals is a very labor-intensive thing. Consequently, there has been an urgent surge of interest to develop an algorithm that scores pawpularity of animals. However, the dataset in Kaggle not only has images, but also metadata describing images. Most methods basically focus on the most advanced image regression methods in recent years, but there is no good method to deal with the metadata of images. To address the above challenges, the paper proposes an image regression model called PETS-SWINF that considers metadata of the images. Our results based on a dataset of Kaggle competition, "PetFinder.my", show that PETS-SWINF has an advantage over only based images models. Our results shows that the RMSE loss of the proposed model on the test dataset is 17.71876 but 17.76449 without metadata. The advantage of the proposed method is that PETS-SWINF can consider both low-order and high-order features of metadata, and adaptively adjust the weights of the image model and the metadata model. The performance is promising as our leadboard score is ranked 15 out of 3545 teams (Gold medal) currently for 2021 Kaggle competition on the challenge "PetFinder.my".

show abstract

CRNet: Classification and Regression Neural Network for Facial Beauty Prediction

Cited by 23 publications

References 21 publications

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

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

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

PETS-SWINF: A regression method that considers images with metadata based Neural Network for pawpularity prediction on 2021 Kaggle Competition "PetFinder.my"

Contact Info

Product

Resources

About