Face Recognition Based on Full Convolutional Neural Network Based on Transfer Learning Model

Zhongkui Fan1 and Ye-peng Guan1, 2

  1. School of Communication and Information Engineering
    Shanghai University, 200444 Shanghai, China
    {fanzkui, ypguan}@shu.edu.cn
  2. Key Laboratory of Advanced Displays and System Application
    Ministry of Education, 200444 shanghai, China

Abstract

Deep learning has achieved a great success in face recognition (FR), however, little work has been done to apply deep learning for face photo-sketch recognition. This paper proposes an adaptive scale local binary pattern extraction method for optical face features. The extracted features are classified by Gaussian process. The most authoritative optical face test set LFW is used to train the trained model. Test, the test accuracy is 98.7%. Finally, the face features extracted by this method and the face features extracted from the convolutional neural network method are adapted to sketch faces through transfer learning, and the results of the adaptation are compared and analyzed. Finally, the paper tested the open-source sketch face data set CUHK Face Sketch database(CUFS) using the multimedia experiment of the Chinese University of Hong Kong. The test result was 97.4%. The result was compared with the test results of traditional sketch face recognition methods. It was found that the method recognized High efficiency, it is worth promoting.

Key words

transfer learning, convolutional neural network, face recognition, adaptive scale, optical face features

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS200922028F

Publication information

Volume 18, Issue 4 (September 2021)
Year of Publication: 2021
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

DownloadAvailable in PDF
Portable Document Format

How to cite

Fan, Z., Guan, Y.: Face Recognition Based on Full Convolutional Neural Network Based on Transfer Learning Model. Computer Science and Information Systems, Vol. 18, No. 4, 1395–1409. (2021), https://doi.org/10.2298/CSIS200922028F