Facial Age Recognition Based on Deep Manifold Learning
Most of existing face age recognition methods use deep learning framework to extract face features to identify age,but high-dimensional face features extracted by deep learning methods often contain a lot of redundant information,which is not conducive to face age recognition.In order to improve the accuracy and robustness of face age recognition algorithm,an algorithm based on Deep Manifold Learning(DML)is proposed.DML first uses deep learning to extract face features,and then selects discriminative face features through manifold Learning,that is,high-dimensional face features extracted by deep learning are embedded into a low-dimensional discriminant subspace to identify age.Experiments on the DML algorithm are carried out on the public face databases MORPH and FG-NET.Experi-ment results show that the Mean Absolute Error(MAE)of DML is significantly reduced,and the Cumulative Score(CS)is significantly improved under different error values,which is significantly superior to current popular face age recognition methods.
age recognitionmanifold learningdeep learningconvolutional neural networkfeature extractionMAE
国家科技期刊平台
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