Nonlinear, flexible, semisupervised learning scheme for face beauty scoring

Dornaika, Fadi; Elorza, Anne; Wang, Kunwei; Arganda‐Carreras, Ignacio

doi:10.1117/1.jei.28.4.043013

Cited by 5 publications

(4 citation statements)

References 31 publications

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“…Captures relative features of beauty by comparing the image directly to the anchor image R3CNN [28] Siamese network technological sophistication, a critical limitation persisted: the reliance on absolute beauty scores [16], [25], [29], [30]. Such scores, by their very nature, encapsulate a monolithic and somewhat reductive perspective on beauty, sidelining the nuanced and inherently subjective dimensions that characterize human aesthetic judgment.…”

Section: Semi-supervisedmentioning

confidence: 99%

“…Semi-supervised learning models [25], [26], [29], [43], [44], including NFME [25] and MSMFME [26], underscore the value of graph-based and multi-view graph fusion techniques in reinforcing model training without additional labeled images. Despite their innovative approach, these models face limitations in computing similarity graphs before estimating beauty predictions, indicating a gap in capturing the relative aspects of beauty.…”

Section: Related Workmentioning

confidence: 99%

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Anchor-Net: Distance-Based Self-Supervised Learning Model for Facial Beauty Prediction

Bae,

Buu,

Lee

2024

IEEE Access

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In today's society, beauty is more than just aesthetics, it has a profound impact on many aspects of life, including social interactions, self-confidence, and job opportunities. To quantify this beauty, the field of Facial Beauty Prediction (FBP) is gaining traction. In the field of FBP, traditional methods often fall short due to their reliance on absolute beauty scores, which do not fully capture the subjective nature of human aesthetic perception. This study presents a novel approach to address this gap through the development of Anchor-Net, a self-supervised learning model that predicts differences in relative beauty scores by comparing images. The objective of this research is to offer a more nuanced understanding of facial beauty by employing a reference image (anchor) alongside a prediction image, thereby aligning closer with how humans perceive aesthetic differences. To construct Anchor-Net, we first developed a Base model that predicts beauty scores using a model pre-trained with VGGFace2. This Base model was then adapted into Anchor-Net, which is designed to train on the difference in beauty scores between a reference image and a prediction image. Our methodology involved two transfer learning steps to leverage the strengths of pre existing models while tailoring them to our specific research problem. The experimental validation of Anchor-Net was conducted on the SCUT-FBP5500 benchmark dataset, utilizing a 6:4 training-testing split and 5-fold cross-validation to ensure robust testing of the model's predictive capabilities. The results demonstrate that Anchor-Net outperforms other state-of-the-art deep learning algorithms on all metrics: Pearson Correlation (PC), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Anchor-Net outperformed other models with a PC of 0.0021, MAE of 0.0055, and RMSE of 0.0065 on a 6:4 training-test split. On average, it achieved a PC of 0.0034, MAE of 0.0155, and RMSE of 0.0135 on 5-fold cross-validation. This research proposes a novel approach to FBP and suggests a broader application of relative comparison methodologies in fields where absolute measurements fall short.

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Section: Semi-supervisedmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Anchor-Net: Distance-Based Self-Supervised Learning Model for Facial Beauty Prediction

Bae,

Buu,

Lee

2024

IEEE Access

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“…By integrating multiple graphs in the label propagation framework, many works succeeded in improving the performance of these algorithms [28,32,37,42]. The main notations used in our paper are shown in Table 1.…”

Section: Related Work 21 Multi-view Graph-based Semi-supervised Learningmentioning

confidence: 99%

“…More recently, efforts to automatically assess the beauty of faces have shifted to manifold-based semi-supervised learning. In the field of machine learning, semi-supervised learning has indeed proven useful when relatively few labelled training samples are available; however, a large number of unlabelled samples are available [27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Multi-View Graph Fusion for Semi-Supervised Learning: Application to Image-Based Face Beauty Prediction

Dornaika

Moujahid

2022

Algorithms

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Facial Beauty Prediction (FBP) is an important visual recognition problem to evaluate the attractiveness of faces according to human perception. Most existing FBP methods are based on supervised solutions using geometric or deep features. Semi-supervised learning for FBP is an almost unexplored research area. In this work, we propose a graph-based semi-supervised method in which multiple graphs are constructed to find the appropriate graph representation of the face images (with and without scores). The proposed method combines both geometric and deep feature-based graphs to produce a high-level representation of face images instead of using a single face descriptor and also improves the discriminative ability of graph-based score propagation methods. In addition to the data graph, our proposed approach fuses an additional graph adaptively built on the predicted beauty values. Experimental results on the SCUTFBP-5500 facial beauty dataset demonstrate the superiority of the proposed algorithm compared to other state-of-the-art methods.

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