Age And Gender Classification
12 papers with code • 2 benchmarks • 4 datasets
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 )
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Most implemented papers
Rank consistent ordinal regression for neural networks with application to age estimation
In many real-world prediction tasks, class labels include information about the relative ordering between labels, which is not captured by commonly-used loss functions such as multi-category cross-entropy.
Age and Gender Classification using Convolutional Neural Networks
Automatic age and gender classification has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media.
Increasingly Packing Multiple Facial-Informatics Modules in A Unified Deep-Learning Model via Lifelong Learning
Simultaneously running multiple modules is a key requirement for a smart multimedia system for facial applications including face recognition, facial expression understanding, and gender identification.
Quantifying Facial Age by Posterior of Age Comparisons
We introduce a novel approach for annotating large quantity of in-the-wild facial images with high-quality posterior age distribution as labels.
A Fusion-based Gender Recognition Method Using Facial Images
This paper proposes a fusion-based gender recognition method which uses facial images as input.
Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile Applications
Automatic age and gender classification based on unconstrained images has become essential techniques on mobile devices.
Multimodal Age and Gender Classification Using Ear and Profile Face Images
Experimental results indicated that profile face images contain a rich source of information for age and gender classification.
Compacting, Picking and Growing for Unforgetting Continual Learning
First, it can avoid forgetting (i. e., learn new tasks while remembering all previous tasks).
Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness Metrics
With the recent expanding attention of machine learning researchers and practitioners to fairness, there is a void of a common framework to analyze and compare the capabilities of proposed models in deep representation learning.
Generalizing MLPs With Dropouts, Batch Normalization, and Skip Connections
A multilayer perceptron (MLP) is typically made of multiple fully connected layers with nonlinear activation functions.