Detection of Human Faces using Neural Networks

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Human face detection is a key technology in machine vision applications including human recognition, access control, security surveillance and so on. This research proposes a precise scheme for human face detection using a hybrid neural network. The system is based on visual information of the face image sequences and is commenced with estimation of the skin area depending on color components. In this paper we have considered HSV and YCbCr color space to extract the visual features. These features are used to train the hybrid network consisting of a bidirectional associative memory (BAM) and a back propagation neural network (BPNN). The BAM is used for dimensional reduction and the multi-layer BPNN is used for training the facial color features. Our system provides superior performance comparable to the existing methods in terms of both accuracy and computational efficiency. The low computation time required for face detection makes it suitable to be employed in real time applications.
Original languageEnglish
Title of host publicationICONIP 2016
Subtitle of host publication23rd Conference proceedings
Place of PublicationSwitzerland
Number of pages9
ISBN (Electronic)9783319466712
Publication statusPublished - 2016
EventInternational Conference on Neural Information Processing - Kyoto, Japan, Japan
Duration: 16 Oct 201621 Oct 2016


ConferenceInternational Conference on Neural Information Processing

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    Chowdhury, M. M. H., Gao, J., & Islam, MD. R. (2016). Detection of Human Faces using Neural Networks. In ICONIP 2016: 23rd Conference proceedings (Vol. 9948, pp. 690-698). Springer.