earticle

논문검색

A Distributed Face Recognition Framework Based on Data Fusion

초록

영어

To accomplish face recognition more efficiently, a distributed face recognition framework based on MB-LBP features and data fusion is presented in this paper. Firstly, four face regions are interactively marked and the Multi-scale Block Local Binary Patterns are extracted from these regions to achieve both locally and globally informative features. Secondly, a distributed framework is introduced to accelerate the recognition process, in which features of each single face region are utilized to perform face classification in parallel. The final decision is made by a kind of data fusion mechanism based on an artificial neural network (ANN) to make rational use of the confidence information got from the classification of each region. In experiment, the runtime and recognition performance of our system is compared with several other popular face recognition paradigms. The results indicate that the distributed framework presented in this paper can promote the efficiency of face recognition prominently while not losing accuracy in recognition performance.

목차

Abstract
 1. Introduction
 2. Face Database
 3. Feature Extraction
  3.1. MB-LBP
  3.2. Partitions
  3.3. MB-LBP Composite Features
 4. Classification
  4.1. SVM Classification
  4.2. Data Fusion Based on Artificial Neural Network
 5. A Distributed Framework for Face Recognition
 6. Experimental Results
 7. Conclusions and Future Works
 Acknowledgements
 References

저자정보

  • Zheng Zhang College of Computer Science and Software, Tianjin Polytechnic University, Tianjin, China, 300387
  • Yan Guo School of Computer Software, Tianjin University, Tianjin, China, 300072
  • Guozhi Song College of Computer Science and Software, Tianjin Polytechnic University, Tianjin, China, 300387

참고문헌

자료제공 : 네이버학술정보

    ※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.

    0개의 논문이 장바구니에 담겼습니다.