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Improving Borderline Adulthood Facial Age Estimation through Ensemble Learning

https://doi.org/10.1145/3339252.3341491
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/https://dx.doi.org/10.6028/nist.ir.7807openalex-snapshot
NIST Special Databse 32 - Multiple Encounter Dataset II (MEDS-II)
inferredcites →https://doi.org/10.1007/978-1-4615-0377-4openalex-snapshot
Interdisciplinary Computing in Java Programming
inferredcites →https://doi.org/10.1007/978-1-4615-0377-4_5openalex-snapshot
Artificial Neural Network
inferredcites →https://doi.org/10.1007/978-1-4842-1043-7_14openalex-snapshot
Microsof t Azure Machine Learning
inferredcites →https://doi.org/10.1007/s00414-013-0875-yopenalex-snapshot
The juvenile face as a suitable age indicator in child pornography cases: a pilot study on the reliability of automated and visual estimation approaches
inferredcites →https://doi.org/10.1007/s11042-010-0635-7openalex-snapshot
Bag of soft biometrics for person identification
inferredcites →https://doi.org/10.1007/s11263-016-0940-3openalex-snapshot
Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks
inferredcites →https://doi.org/10.1016/j.diin.2018.04.024openalex-snapshot
Deep learning at the shallow end: Malware classification for non-domain experts
inferredcites →https://doi.org/10.1016/j.diin.2019.01.024openalex-snapshot
Improving the accuracy of automated facial age estimation to aid CSEM investigations
inferredcites →https://doi.org/10.1016/j.neucom.2015.09.115openalex-snapshot
Automatic age estimation based on deep learning algorithm
inferredcites →https://doi.org/10.1016/j.scijus.2016.08.005openalex-snapshot
Juvenile age estimation from facial images
inferredcites →https://doi.org/10.1016/s0262-8856(97)00070-xopenalex-snapshot
The FERET database and evaluation procedure for face-recognition algorithms
inferredcites →https://doi.org/10.1109/ares.2015.81openalex-snapshot
Overview of the Forensic Investigation of Cloud Services
inferredcites →https://doi.org/10.1109/cvpr.2017.463openalex-snapshot
Age Progression/Regression by Conditional Adversarial Autoencoder
inferredcites →https://doi.org/10.1109/cvpr.2017.86openalex-snapshot
Using Ranking-CNN for Age Estimation
inferredcites →https://doi.org/10.1109/cvprw.2015.7301352openalex-snapshot
Age and gender classification using convolutional neural networks
inferredcites →https://doi.org/10.1109/icb.2013.6613022openalex-snapshot
Age estimation from face images: Human vs. machine performance
inferredcites →https://doi.org/10.1109/icpr.2008.4761297openalex-snapshot
RUSBoost: Improving classification performance when training data is skewed
inferredcites →https://doi.org/10.1109/ijcb.2011.6117601openalex-snapshot
Contourlet appearance model for facial age estimation
inferredcites →https://doi.org/10.1109/mipro.2016.7522353openalex-snapshot
Creating a face database for age estimation and classification
inferredcites →https://doi.org/10.1109/sadfe33392.2013openalex-snapshot
2013 8th International Workshop on Systematic Approaches to Digital Forensics Engineering (SADFE)
inferredcites →https://doi.org/10.1109/spw.2018.00028openalex-snapshot
Evaluating Automated Facial Age Estimation Techniques for Digital Forensics
inferredcites →https://doi.org/10.1109/tifs.2014.2359646openalex-snapshot
Age and Gender Estimation of Unfiltered Faces
inferredcites →https://doi.org/10.1109/tpami.2010.36openalex-snapshot
Age Synthesis and Estimation via Faces: A Survey
inferredcites →https://doi.org/10.1145/1553374.1553399openalex-snapshot
EigenTransfer
inferredcites →https://doi.org/10.1145/2812802openalex-snapshot
YFCC100M
inferredcites →https://doi.org/10.1177/1079063217724768openalex-snapshot
The Challenges of Identifying and Classifying Child Sexual Abuse Material
inferredcites →https://doi.org/10.6028/nist.ir.6264openalex-snapshot
The FERET evaluation methodology for face-recognition algorithms

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4833243openalex-extracted
Computer Vision for Multimedia Geolocation in Human Trafficking Investigation: A Systematic Literature Review
inferred← citeshttps://doi.org/10.1016/j.fsidi.2020.300921openalex-snapshot
DeepUAge: Improving Underage Age Estimation Accuracy to Aid CSEM Investigation
inferred← citeshttps://doi.org/10.1016/j.fsidi.2021.301119openalex-snapshot
Vec2UAge: Enhancing underage age estimation performance through facial embeddings
inferred← citeshttps://doi.org/10.1016/j.measurement.2025.117374openalex-snapshot
Water content intelligent measurement method of detection robot for deep soils within loess slopes
inferred← citeshttps://doi.org/10.1109/cybersecurity49315.2020.9138851openalex-snapshot
Assessing the Influencing Factors on the Accuracy of Underage Facial Age Estimation
inferred← citeshttps://doi.org/10.1145/3555186openalex-snapshot
Ethical Tensions in Applications of AI for Addressing Human Trafficking: A Human Rights Perspective
inferred← citeshttps://doi.org/10.12785/ijcds/140118openalex-snapshot
Gender and Age Estimation from Human Faces Based on Deep Learning Techniques: A Review
inferred← citeshttps://doi.org/10.18239/jornadas_2021.34.25openalex-snapshot
Reinforcement of age estimation in forensic tools to detect Child Sexual Exploitation Material
inferred← citeshttps://doi.org/10.3389/fpsyt.2026.1595063openalex-snapshot
Adopting a user-centred design approach for the development of on-device technology to prevent the viewing of child sexual abuse material: app design insights and principles from the development of ‘Salus’
inferred← citeshttps://doi.org/10.3390/s20164491openalex-snapshot
Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications
inferred← citeshttps://doi.org/10.3390/s20205850openalex-snapshot
Rectification and Super-Resolution Enhancements for Forensic Text Recognition
inferred← citeshttps://doi.org/10.3390/sym13081497openalex-snapshot
StyleGANs and Transfer Learning for Generating Synthetic Images in Industrial Applications
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