SOTAVerified

Age And Gender Classification

Age and gender classification is a dual-task of identifying the age and gender of a person from an image or video.

( Image credit: Multi-Expert Gender Classification on Age Group by Integrating Deep Neural Networks )

Papers

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Showing 1–34 of 34 papers

TitleStatusHype
BN-AuthProf: Benchmarking Machine Learning for Bangla Author Profiling on Social Media TextsCode0
A Hybrid Transformer-Sequencer approach for Age and Gender classification from in-wild facial images—0
Beyond Specialization: Assessing the Capabilities of MLLMs in Age and Gender EstimationCode3
MiVOLO: Multi-input Transformer for Age and Gender EstimationCode2
Moving Window Regression: A Novel Approach to Ordinal RegressionCode1
Age and Gender Classification with Small Scale CNN—0
Dynamic Adaptive Spatio-temporal Graph Convolution for fMRI ModellingCode1
Generalizing MLPs With Dropouts, Batch Normalization, and Skip ConnectionsCode1
Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness MetricsCode1
Towards Measuring Fairness in AI: the Casual Conversations Dataset—0
Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware RegressionCode1
Deep Ordinal Regression using Optimal Transport Loss and Unimodal Output Probabilities—0
Compacting, Picking and Growing for Unforgetting Continual LearningCode1
Probabilistic Deep Ordinal Regression Based on Gaussian Processes—0
Multimodal Age and Gender Classification Using Ear and Profile Face ImagesCode0
Increasingly Packing Multiple Facial-Informatics Modules in A Unified Deep-Learning Model via Lifelong LearningCode1
Soft Labels for Ordinal Regression—0
Rank consistent ordinal regression for neural networks with application to age estimationCode1
Multi-Expert Gender Classification on Age Group by Integrating Deep Neural Networks—0
Age and Gender Classification From Ear Images—0
Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile ApplicationsCode0
Fine-Grained Age Estimation in the wild with Attention LSTM Networks—0
SAF- BAGE: Salient Approach for Facial Soft-Biometric Classification - Age, Gender, and Facial Expression—0
A Fusion-based Gender Recognition Method Using Facial ImagesCode0
Age Group and Gender Estimation in the Wild with Deep RoR Architecture—0
Quantifying Facial Age by Posterior of Age ComparisonsCode0
Understanding and Comparing Deep Neural Networks for Age and Gender Classification—0
Local Deep Neural Networks for Age and Gender Classification—0
ConvNets with Smooth Adaptive Activation Functions for Regression—0
How to Train Your Deep Neural Network with Dictionary Learning—0
How Transferable are CNN-based Features for Age and Gender Classification?—0
SPA: Web-based Platform for easy Access to Speech Processing Modules—0
Age and Gender Classification using Convolutional Neural NetworksCode0
Shape Driven Kernel Adaptation in Convolutional Neural Network for Robust Facial Traits Recognition—0
Show:102550
All datasetsAdience AgeAdience GenderBN-AuthProf

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViT-hSeqAccuracy (5-fold)84.91—Unverified
2MiVOLO-V2Accuracy (5-fold)69.43—Unverified
3MiVOLO-D1Accuracy (5-fold)68.69—Unverified
4AL-ResNets-34 + IMDB-WIKIAccuracy (5-fold)67.47—Unverified
5R-SAAFc2 +IMDB-WIKIAccuracy (5-fold)67.3—Unverified
6RoR-34 + IMDB-WIKIAccuracy (5-fold)66.74—Unverified
7MWRAccuracy (5-fold)62.6—Unverified
8UNIORD-ResNet-101 (single crop, pytorch)Accuracy (5-fold)61—Unverified
9RetinaFace + ArcFace + MLP + IC + Skip connectionsAccuracy (5-fold)60.86—Unverified
10CPG (single crop, pytorch)Accuracy (5-fold)57.66—Unverified
#ModelMetricClaimedVerifiedStatus
1MiVOLO-V2Accuracy (5-fold)97.39—Unverified
2ViT-hSeqAccuracy (5-fold)96.56—Unverified
3MiVOLO-D1Accuracy (5-fold)96.51—Unverified
4RetinaFace + ArcFace + MLP + Skip connectionsAccuracy (5-fold)90.66—Unverified
5CPG (single crop, pytorch)Accuracy (5-fold)89.66—Unverified
6PAENet (single crop, tensorflow)Accuracy (5-fold)89.08—Unverified
7Levi_Hassner CNN ( over-sample, caffe)Accuracy (5-fold)86.8—Unverified
8Levi_Hassner CNN (single crop, caffe)Accuracy (5-fold)85.9—Unverified
9LMTCNN-2-1 (single crop, tensorflow)Accuracy (5-fold)85.16—Unverified
10Levi_Hassner CNN (single crop, tensorflow)Accuracy (5-fold)82.52—Unverified
#ModelMetricClaimedVerifiedStatus
1Multinomial Naive Bayes (MNB)F1 score0.91—Unverified