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Author:

Deng, Wanghua (Deng, Wanghua.) | Liang, Xun (Liang, Xun.)

Indexed by:

CPCI-S

Abstract:

Face detection is a branch field originated from face recognition. In recent years, face detection has shown important significance and value in daily produce and application. A face detection method based on BP neural network and improved AdaBoost algorithm is proposed in this paper. First, Using BP neural network instead of YCbCr gaussian model to build skin color model. Meanwhile, a new method of weight updating for AdaBoost algorithm is proposed. The distance between the threshold and the sample is introduced into the update of weight. Besides the weight has a boundary value. Finally, BP neural network is used to obtain the skin color candidate areas in the image, and the improved AdaBoost algorithm is used to accurately detect the face in the image. Based on our experimental result, the new solution using our BP neural network and improved AdaBoost algorithm performs higher accuracy than the existing approach.

Keyword:

AdaBoost YCbCr Gaussian model Face detection BP neural network

Author Community:

  • [ 1 ] [Deng, Wanghua]Beijing Univ Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Liang, Xun]Beijing Univ Technol, Beijing 100124, Peoples R China

Reprint Author's Address:

  • [Deng, Wanghua]Beijing Univ Technol, Beijing 100124, Peoples R China

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Source :

PROCEEDINGS OF 2018 5TH IEEE INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND INTELLIGENCE SYSTEMS (CCIS)

ISSN: 2376-5933

Year: 2018

Page: 395-399

Language: English

Cited Count:

WoS CC Cited Count: 3

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 10

Affiliated Colleges:

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