Detection of human faces using neural networks

Mohammad Mozammel Hoque Chowdhury, Junbin Gao, MD Rafiqul Islam

Research output: Book chapter/Published conference paperConference paperpeer-review

1 Citation (Scopus)
6 Downloads (Pure)


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 publicationNeural Information Processing
Subtitle of host publication23rd International Conference, ICONIP 2016 Kyoto, Japan, October 16–21, 2016 Proceedings, Part II
Place of PublicationSwitzerland
Number of pages9
ISBN (Electronic)9783319466729
ISBN (Print)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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