Facial Recognition via Transfer Learning: Fine-tuning Keras_vggface

被引：2

作者：

Luttrell, Joseph ^{[1
]}

Zhou, Zhaoxian ^{[1
]}

Zhang, Chaoyang ^{[1
]}

Gong, Ping ^{[2
]}

Zhang, Yuanyuan ^{[3
]}

机构：

[1] Univ Southern Mississippi, Sch Comp, Hattiesburg, MS 39406 USA

[2] US Army Engineer Res & Dev Ctr, Environm Lab, Vicksburg, MS USA

[3] Univ Southern Mississippi, Ctr Logist Trade & Transportat, Hattiesburg, MS USA

来源：

PROCEEDINGS 2017 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE (CSCI) | 2017年

关键词：

convolutional neural networks; facial recognition; transfer learning; FERET image dataset;

D O I：

10.1109/CSCI.2017.98

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The challenge of developing facial recognition systems has been the focus of many research efforts in recent years and has numerous applications in areas such as security, entertainment, and biometrics. Recently, most progress in this field has come from training very deep neural networks on massive datasets. Here, we use a pre-trained face recognition model and perform transfer learning to produce a network that is capable of making accurate predictions on a much smaller dataset. We also compare our results with results produced by a selection of classical algorithms on the same dataset.

引用

页码：576 / 579

页数：4

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