Transfer Learning with Deep CNNs for Gender Recognition and Age Estimation
This work addresses image-based demographic analysis, but it is incremental as it applies existing methods with training optimizations.
The paper tackled gender recognition and age estimation from images using transfer learning with pretrained deep CNNs, achieving a gender recognition accuracy of 98.7% and a mean absolute error of 4.1 years for age estimation.
In this project, competition-winning deep neural networks with pretrained weights are used for image-based gender recognition and age estimation. Transfer learning is explored using both VGG19 and VGGFace pretrained models by testing the effects of changes in various design schemes and training parameters in order to improve prediction accuracy. Training techniques such as input standardization, data augmentation, and label distribution age encoding are compared. Finally, a hierarchy of deep CNNs is tested that first classifies subjects by gender, and then uses separate male and female age models to predict age. A gender recognition accuracy of 98.7% and an MAE of 4.1 years is achieved. This paper shows that, with proper training techniques, good results can be obtained by retasking existing convolutional filters towards a new purpose.