检索结果-内蒙古大学图书馆

Is facial beauty in the eyes? A multi-method approach to interpreting facial beauty prediction in machine learning models

引用

Discover Artificial Intelligence 2025年第1期5卷 1-18页

作者： Ibrahim, Ahmed Aman Ugail, Noah Hassan Ugail, Hassan Centre for Visual Computing and Intelligent Systems University of Bradford Bradford United Kingdom Department of Mathematical Sciences University of Bath Bath United Kingdom

Despite advances in facial beauty prediction, how specific facial regions contribute to perceptions of attractiveness remains largely unexplored, highlighting a critical interpretability gap in this domain. This study addresses the interpretability gap in facial beauty prediction (FBP) models by introducing a novel framework that combines global and local interpretability methods. We introduce Region Attribution, a technique that aggregates XRAI (eXplanation with Ranked Area Integrals) saliency maps across predefined facial regions to quantify their relative importance in individual predictions. Two global approaches complement this local interpretability: permutation feature importance, which systematically explores individual facial regions across the dataset to measure performance degradation, and individual feature prediction, where separate CNN models are trained on isolated facial regions to assess their independent predictive power. Using the SCUT-FBP5500 and MEBeauty datasets, we train convolutional neural networks on both full faces and individual facial features. While our findings reveal slight variations in feature rankings across the three methods, they consistently identify the eyes and nose regions as crucial determinants in facial beauty prediction. Thus, this study demonstrates the value of a multi-method approach in understanding the complex interplay of facial features in beauty prediction machine learning models. © The Author(s) 2025.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Facial Beauty Analysis Using Distribution Prediction and CNN Ensembles 15

Facial Beauty Analysis Using Distribution Prediction and CNN...

引用

15th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2023

作者： Ibrahim, Ahmed Aman Ugail, Noah Hassan Jayatileke, Tazkia Hoodh Saffery, Millie Hope Ugail, Hassan University of Bradford Centre for Visual Computing and Intelligent Systems Bradford United Kingdom University of Bath Department of Mathematical Sciences Bath United Kingdom The Circle by Ugail Foundation Ugail Foundation Male Maldives

ISBN: (纸本)9798350316551

Facial Beauty Prediction (FBP) is a computer vision task of quantifying the beauty of a face. Several solutions to this problem have benefitted immensely from the recent developments in deep learning. However, the majority of current methods train machine learning models to purely predict mean beauty scores, treating FBP solely as a regression task. In addition, deep learning based FBP approaches so far use transfer learning from models trained on general classification tasks such as ImageNet. We propose fine-tuning an ensemble of convolutional neural network (CNN) models originally trained on face verification tasks using a variety of loss functions such as Earth Mover's Distance (EMD) based loss. With this approach, our method can predict the entire beauty score distribution rather than just the mean, and the predicted mean scores have a higher Pearson Correlation (PC) compared to the ground truth scores. This method achieves state of the art results on the MEBeauty dataset in terms of mean absolute error, root mean squared error and PC between the predicted and the ground truth mean scores. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

On Handcrafted Machine Learning Features for Art Authentication

On Handcrafted Machine Learning Features for Art Authenticat...

引用

IEEE International Conference on Signal and Image Processing Applications (ICSIPA)

作者： M. Bwanali H. Ugail Z. Mnasri E. Jensen Z. Omar Centre for Visual Computing and Intelligent Systems University of Bradford Bradford United Kingdom Faculty of Electrical Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia

ISBN: (数字)9798350352368

ISBN: (纸本)9798350352375

Accurate art authentication is crucial for ensuring correct attribution, cultural preservation, and financial valuation of artworks. Machine learning tools can significantly complement established techniques such as connoisseurship, provenance research, and scientific analysis (e.g., pigment analysis and radiographic imaging). This study explores a more objective and explainable approach to machine learning in art authentication. Specifically, we investigate the use of handcrafted features, such as image entropy, energy, texture patterns, and image similarities, to train a OneClassSVM model based on known authentic images of paintings by a given artist. To demonstrate the versatility of our method, we present results for three selected artists: John Constable, Vincent van Gogh, and Jean-Michel Basquiat. We further show by way of an example how our method can be utilised to study a given painting in terms of its attribution to a given artist. These examples, along with the accuracy of our results, indicate the potential of the proposed method as a flexible and efficient tool for art authentication alongside more established methods.

关键词： Art Accuracy Authentication Machine learning Feature extraction Entropy Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Fine-Tuned Face Recognition Models for Sibling Discrimination

Fine-Tuned Face Recognition Models for Sibling Discriminatio...

引用

Electrics and Computer (INTCEC), Interdisciplinary Conference on

作者： Rita Goel Maida Alamgir Haroon Wahab Irfan Mehmood Hassan Ugail Centre for Visual Computing University of Bradford Bradford United Kingdom Analytics Faculty of Mgmt. Law and Social Science University of Bradford Bradford United Kingdom Centre for Visual Computing & Intelligent Systems School of Computer Science AI and Electronics University of Bradford Bradford England UK

ISBN: (数字)9798350349450

ISBN: (纸本)9798350349467

Face recognition has witnessed remarkable progress in recent years, with applications ranging from security systems to social media platforms. This work introduces a novel approach to the development of face recognition models specifically tailored for the challenging task of sibling discrimination. Recognising subtle differences among individuals who share familial resemblance poses a unique set of challenges for conventional face recognition systems. Four highly developed models used for face recognition, namely, VGG19, VGG16, VGGFace, and FaceNet, are fine-tuned with a Softmax activation function to classify images as same or different. Performance metrics derived from custom datasets show that the proposed approach yields robust and realistic results compared to pre-trained models, with varying accuracies for different facial parts.

关键词： Accuracy Social networking (online) Face recognition Computational modeling Distance measurement Security Task analysis

来源：评论

学校读者我要写书评

暂无评论

Facial Beauty Analysis Using Distribution Prediction and CNN Ensembles

Facial Beauty Analysis Using Distribution Prediction and CNN...

引用

International Conference on Software, Knowledge Information, Industrial Management and Applications (SKIMA)

作者： Ahmed Aman Ibrahim Noah Hassan Ugail Tazkia Hoodh Jayatileke Millie Hope Saffery Hassan Ugail Centre for Visual Computing and Intelligent Systems University of Bradford Bradford United Kingdom Department of Mathematical Sciences University of Bath Bath United Kingdom theCircle by Ugail Foundation Ugail Foundation Male’ Maldives

关键词：

来源：评论

学校读者我要写书评

暂无评论

Towards Holistic Analysis of Wireless Capsule Endoscopic Videos: A Taxonomy-Driven Machine Learning Framework for Clinically Comprehensive WCE Frame Level Analysis

Towards Holistic Analysis of Wireless Capsule Endoscopic Vid...

引用

Electrics and Computer (INTCEC), Interdisciplinary Conference on

作者： Haroon Wahab Rita Goel Maida Alamgir Irfan Mehmood Khan Muhammad Hassan Ugail Centre for Visual Computing & Intelligent Systems School of Computer Science AI and Electronics University of Bradford Bradford England UK Department of Applied Artificial Intelligence Visual Analytics Knowledge Laboratory (VIS2KNOW) Sungkyunkwan University Seoul Republic of Korea

ISBN: (数字)9798350349450

ISBN: (纸本)9798350349467

Manual perusal of capsule endoscopic videos is laborious due to their length, redundancy, and poor quality frames, risking misdetection. Limited representation of diverse anomalies and subtle lesions led to less generalized approaches targeting only individual or few anomalous findings. To deploy automated analysis in clinical settings effectively, a holistic, end-to-end approach is crucial, distinguishing between frame-level analysis and disease-level diagnosis. This study proposes a holistic framework for frame-level analysis based on a taxonomy of findings, leveraging clinical consensus. Clinical knowledge implicit in the taxonomy can enhance Computer Aided Diagnostics (CAD) systems. Convolutional Neural Networks (CNNs) trained at appropriate granularity levels in the taxonomy show improved performance metrics. Validation on the Kvasir-capsule dataset demonstrates a 2-4% performance boost. Taxonomy-based inference scores effectively resolve mimicked anomalies without additional learning.

关键词： Training Wireless communication Measurement Taxonomy Redundancy Merging Manuals

来源：评论

学校读者我要写书评

暂无评论

Biomedical image analysis competitions: The state of current participation practice

arXiv

引用

arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

Why is the Winner the Best?

Why is the Winner the Best?

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：