Understanding and Comparing Deep Neural Networks for Age and Gender Classification
Recently, deep neural networks have demonstrated excellent performances in recognizing the age and gender on human face images. However, these models were applied in a black-box manner with no information provided about which facial features are actually used for prediction and how these features depend on image preprocessing, model initialization and architecture choice. We present a study invest
Age and Gender Classification Using Convolutional Neural Networks
Faces from the Adience benchmark for age and gender classification. These images represent some of the challenges of age and gender estimation from real-world, unconstrained images. Most notably, extreme blur (low-resolution), occlusions, out-of-plane pose variations, expressions and more.. AbstractAutomatic age and gender classification has become relevant to an increasing amount of applications,
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Controllable Face Aging
Mean-Variance Loss for Deep Age Estimation From a Face
GitHub - LouieYang/AgeAndGenderEstimation: age and gender estimation using caffe network
