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

Showing 125 of 34 papers

TitleStatusHype
Beyond Specialization: Assessing the Capabilities of MLLMs in Age and Gender EstimationCode3
MiVOLO: Multi-input Transformer for Age and Gender EstimationCode2
Rank consistent ordinal regression for neural networks with application to age estimationCode1
Moving Window Regression: A Novel Approach to Ordinal RegressionCode1
Increasingly Packing Multiple Facial-Informatics Modules in A Unified Deep-Learning Model via Lifelong LearningCode1
Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware RegressionCode1
Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness MetricsCode1
Dynamic Adaptive Spatio-temporal Graph Convolution for fMRI ModellingCode1
Generalizing MLPs With Dropouts, Batch Normalization, and Skip ConnectionsCode1
Compacting, Picking and Growing for Unforgetting Continual LearningCode1
BN-AuthProf: Benchmarking Machine Learning for Bangla Author Profiling on Social Media TextsCode0
Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile ApplicationsCode0
Age and Gender Classification using Convolutional Neural NetworksCode0
Multimodal Age and Gender Classification Using Ear and Profile Face ImagesCode0
A Fusion-based Gender Recognition Method Using Facial ImagesCode0
Quantifying Facial Age by Posterior of Age ComparisonsCode0
SPA: Web-based Platform for easy Access to Speech Processing Modules0
Towards Measuring Fairness in AI: the Casual Conversations Dataset0
Understanding and Comparing Deep Neural Networks for Age and Gender Classification0
Age and Gender Classification From Ear Images0
Age and Gender Classification with Small Scale CNN0
Age Group and Gender Estimation in the Wild with Deep RoR Architecture0
A Hybrid Transformer-Sequencer approach for Age and Gender classification from in-wild facial images0
ConvNets with Smooth Adaptive Activation Functions for Regression0
Deep Ordinal Regression using Optimal Transport Loss and Unimodal Output Probabilities0
Show:102550
← PrevPage 1 of 2Next →

Benchmark Results

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