Facial Peculiarity Retrieval via Deep Neural Networks Fusion

DOI

10.2991/ijcis.11.1.5How to use a DOI?

Keywords

face retrieval; clustering analysis; ASM; deep learning; DNN

Abstract

Face retrieval is becoming increasingly useful and important for security maintenance operations. In actual applications, face retrieval is usually influenced by some changeable site conditions, such as various postures, expressions, camera angles, illuminations, and so on. In this paper, facial peculiar features are extracted and classified by dynamically integrated deep neural networks (DNNs), in order to enhance the adaptability in actual conditions. Firstly, eight kinds of facial components are detected and located by clustering analysis and Active Shape Model (ASM). Secondly, certain peculiar patterns are defined for each kind of facial component, and eight specialized DNNs are designed to extract features and classify components. Thirdly, the similarity between faces is calculated by dynamically integrating the results of each DNN. Comparative experiments on standard image sets and wild image sets demonstrate that our algorithm outperforms global feature models in retrieval accuracy. Our algorithm is particularly suitable for practical application with regard to natural real videos and images.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - Peiqin Li
AU  - Jianbin Xie
AU  - Zhen Li
AU  - Tong Liu
AU  - Wei Yan
PY  - 2018
DA  - 2018/01/01
TI  - Facial Peculiarity Retrieval via Deep Neural Networks Fusion
JO  - International Journal of Computational Intelligence Systems
SP  - 58
EP  - 65
VL  - 11
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.11.1.5
DO  - 10.2991/ijcis.11.1.5
ID  - Li2018
ER  -