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Face detection based neural networks using robust skin color segmentation

Mohamed, A, Weng, Y, Jiang, J and Ipson, S (2008) Face detection based neural networks using robust skin color segmentation In: SSD 2008, 2008-07-20 - 2008-07-23, Amman, Jordan.

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This paper proposes a robust schema for face detection system via Gaussian mixture model to segment image based on skin color. After skin and non skin face candidatespsila selection, features are extracted directly from discrete cosine transform (DCT) coefficients computed from these candidates. Moreover, the back-propagation neural networks are used to train and classify faces based on DCT feature coefficients in Cb and Cr color spaces. This schema utilizes the skin color information, which is the main feature of face detection. DCT feature values of faces, representing the data set of skin/non-skin face candidates obtained from Gaussian mixture model are fed into the back-propagation neural networks to classify whether the original image includes a face or not. Experimental results shows that the proposed schema is reliable for face detection, and pattern features are detected and classified accurately by the backpropagation neural networks.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Divisions : Surrey research (other units)
Authors :
Mohamed, A
Weng, Y
Ipson, S
Date : 2008
DOI : 10.1109/SSD.2008.4632827
Contributors :
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 12:24
Last Modified : 23 Jan 2020 17:49

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