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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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 11–20 of 34 papers

Title	Date	Tasks	Status	Hype
Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression	Mar 25, 2021	Aesthetics Quality AssessmentAge And Gender Classification	CodeCode Available	1
Deep Ordinal Regression using Optimal Transport Loss and Unimodal Output Probabilities	Nov 15, 2020	Age And Gender Classificationregression	—Unverified	0
Compacting, Picking and Growing for Unforgetting Continual Learning	Oct 15, 2019	Age And Gender ClassificationContinual Learning	CodeCode Available	1
Probabilistic Deep Ordinal Regression Based on Gaussian Processes	Oct 1, 2019	Age And Gender ClassificationAge Estimation	—Unverified	0
Multimodal Age and Gender Classification Using Ear and Profile Face Images	Jul 23, 2019	Age And Gender ClassificationClassification	CodeCode Available	0
Increasingly Packing Multiple Facial-Informatics Modules in A Unified Deep-Learning Model via Lifelong Learning	Jun 10, 2019	Age And Gender ClassificationContinual Learning	CodeCode Available	1
Soft Labels for Ordinal Regression	Jun 1, 2019	Aesthetics Quality AssessmentAge And Gender Classification	—Unverified	0
Rank consistent ordinal regression for neural networks with application to age estimation	Jan 20, 2019	Age And Gender ClassificationAge Estimation	CodeCode Available	1
Multi-Expert Gender Classification on Age Group by Integrating Deep Neural Networks	Sep 6, 2018	Age And Gender ClassificationAge Estimation	—Unverified	0
Age and Gender Classification From Ear Images	Jun 14, 2018	Age And Gender ClassificationAge Classification	—Unverified	0

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All datasets Adience Age Adience Gender BN-AuthProf

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ViT-hSeq	Accuracy (5-fold)	84.91	—	Unverified
2	MiVOLO-V2	Accuracy (5-fold)	69.43	—	Unverified
3	MiVOLO-D1	Accuracy (5-fold)	68.69	—	Unverified
4	AL-ResNets-34 + IMDB-WIKI	Accuracy (5-fold)	67.47	—	Unverified
5	R-SAAFc2 +IMDB-WIKI	Accuracy (5-fold)	67.3	—	Unverified
6	RoR-34 + IMDB-WIKI	Accuracy (5-fold)	66.74	—	Unverified
7	MWR	Accuracy (5-fold)	62.6	—	Unverified
8	UNIORD-ResNet-101 (single crop, pytorch)	Accuracy (5-fold)	61	—	Unverified
9	RetinaFace + ArcFace + MLP + IC + Skip connections	Accuracy (5-fold)	60.86	—	Unverified
10	CPG (single crop, pytorch)	Accuracy (5-fold)	57.66	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MiVOLO-V2	Accuracy (5-fold)	97.39	—	Unverified
2	ViT-hSeq	Accuracy (5-fold)	96.56	—	Unverified
3	MiVOLO-D1	Accuracy (5-fold)	96.51	—	Unverified
4	RetinaFace + ArcFace + MLP + Skip connections	Accuracy (5-fold)	90.66	—	Unverified
5	CPG (single crop, pytorch)	Accuracy (5-fold)	89.66	—	Unverified
6	PAENet (single crop, tensorflow)	Accuracy (5-fold)	89.08	—	Unverified
7	Levi_Hassner CNN ( over-sample, caffe)	Accuracy (5-fold)	86.8	—	Unverified
8	Levi_Hassner CNN (single crop, caffe)	Accuracy (5-fold)	85.9	—	Unverified
9	LMTCNN-2-1 (single crop, tensorflow)	Accuracy (5-fold)	85.16	—	Unverified
10	Levi_Hassner CNN (single crop, tensorflow)	Accuracy (5-fold)	82.52	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Multinomial Naive Bayes (MNB)	F1 score	0.91	—	Unverified