TY - GEN
T1 - Deep convolutional neural networks-based age and gender classification with facial images
AU - Liu, Xuan
AU - Li, Junbao
AU - Hu, Cong
AU - Pan, Jeng Shyang
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - In this paper, we build an age and gender classification system including two networks to classify age and gender based on GoogLeNet with the help of Caffe deep learning framework. It outputs gender and age groups of the facial images captured from the camera. We use Adience dataset to train GoogLeNet. Asynchronous Stochastic Gradient Descent based on multi-GPUs is used to optimize training process. We intend to use the trained network to build a classification system in real world to show the practicability. For instance, it can apply to a targeted delivery in bus stop or department store. The results indicate that the accuracy of the classification network can be improved by pre-training. In addition, the multi-GPUs training platform can improve the training speed during the recognition. Overall system reaches speed of 8fps with a high accuracy to classify age and gender.
AB - In this paper, we build an age and gender classification system including two networks to classify age and gender based on GoogLeNet with the help of Caffe deep learning framework. It outputs gender and age groups of the facial images captured from the camera. We use Adience dataset to train GoogLeNet. Asynchronous Stochastic Gradient Descent based on multi-GPUs is used to optimize training process. We intend to use the trained network to build a classification system in real world to show the practicability. For instance, it can apply to a targeted delivery in bus stop or department store. The results indicate that the accuracy of the classification network can be improved by pre-training. In addition, the multi-GPUs training platform can improve the training speed during the recognition. Overall system reaches speed of 8fps with a high accuracy to classify age and gender.
KW - Deep Conventional Neural Networks
KW - age and gender classification
KW - real-time recognition system
UR - https://www.scopus.com/pages/publications/85048740882
U2 - 10.1109/EIIS.2017.8298719
DO - 10.1109/EIIS.2017.8298719
M3 - 会议稿件
AN - SCOPUS:85048740882
T3 - 1st International Conference on Electronics Instrumentation and Information Systems, EIIS 2017
SP - 1
EP - 4
BT - 1st International Conference on Electronics Instrumentation and Information Systems, EIIS 2017
A2 - Li, Jun-Bao
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st International Conference on Electronics Instrumentation and Information Systems, EIIS 2017
Y2 - 3 June 2017 through 5 June 2017
ER -