Transfer Learning with Deep CNNs for Gender Recognition and Age Estimation

Autor:	Smith, Philip, Chen, Cuixian
Rok vydání:	2018
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: It has been accepted in the 5th National Symposium for NSF REU Research in Data Science, Systems, and Security
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1811.07344 Zobrazit plný text záznamu View this record from Arxiv