Human Face Detection System by Kenzan NET with preprocess analyzing hyperspectral image

Takakazu Chashikawa, Keizo Fujii, Yoshiyasu Takefuji

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes a neural network system to detect human faces. Our scheme is composed of a preprocess and KenzanNET. Preprocessing analyzes hyperspectral images by using a hybrid self-organizing classification model to extract skin area and making a facial candidate pattern based on the extracted skin area. KenzanNET discriminate a face from other body parts. KenzanNET is a kind of feed forward neural network and is made from CombNET [ I[ improved by an additional learning function. Under the various conditions in terms of background and brightness in a room and the distance between people and camera, our system can detect human face with 76.9% accuracy.

Original languageEnglish
Title of host publicationProceedings of the 2nd International Conference on Intelligent Processing and Manufacturing of Materials, IPMM 1999
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages453-458
Number of pages6
Volume1
ISBN (Electronic)0780354893, 9780780354890
DOIs
Publication statusPublished - 1999 Jan 1
Event2nd International Conference on Intelligent Processing and Manufacturing of Materials, IPMM 1999 - Honolulu, United States
Duration: 1999 Jul 101999 Jul 15

Other

Other2nd International Conference on Intelligent Processing and Manufacturing of Materials, IPMM 1999
CountryUnited States
CityHonolulu
Period99/7/1099/7/15

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Materials Science (miscellaneous)

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    Chashikawa, T., Fujii, K., & Takefuji, Y. (1999). Human Face Detection System by Kenzan NET with preprocess analyzing hyperspectral image. In Proceedings of the 2nd International Conference on Intelligent Processing and Manufacturing of Materials, IPMM 1999 (Vol. 1, pp. 453-458). [792522] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IPMM.1999.792522