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Paper Title: Efficient Scale invariant and Back Propagation Neural Network method using LIP Region Segmentation
Authors Name: Shaifali Sharma , Geetanjali Babbar , Ishdeep Singla , Dr. Gagan Jindal
Unique Id: IJSDR1906075
Published In: Volume 4 Issue 6, June-2019
Abstract: With the advent of technological sensor devices and human interface machine technology, there has been extensive research done in lip segmentation methods by several researchers — some linguistic features required for interaction with the machine equipment .Therefore, research work has been done in the audio speech detection scheme for recognition of lip reading .Visual lip reading technology developed based on the extraction of features of the lip. Lip segmentation is essential to approach to recognize lip reading scheme. Meanwhile, it helps improve parameters. Several methods studied to segment the lip area based on localized active contour method using twice contour finding and combined color-space method. Apply the illumination histogram equalization to real color images to reduce the distortion of uneven illumination. The proposed method implemented can get better accuracy rate and segmentation results and compare with the existing process using area or circle as the region to segment grayscale images and combined in the color-space image. The main advantages of this SIFT and BPNN method of this technique is the results of lips because the inner region found. The experiment tool is used MATLAB 2016a and designs a PROJECT APPLICATION. Improve the success rate and reduce the segmented error and compared with the current metrics.
Keywords: Lip Segmentation, Feature Extraction – Scale Invariant Feature Transformation, BPNN – Back Propagation Neural Network, and Active Contour.
Cite Article: "Efficient Scale invariant and Back Propagation Neural Network method using LIP Region Segmentation", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.4, Issue 6, page no.424 - 427, June-2019, Available :http://www.ijsdr.org/papers/IJSDR1906075.pdf
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Publication Details: Published Paper ID: IJSDR1906075
Registration ID:190740
Published In: Volume 4 Issue 6, June-2019
DOI (Digital Object Identifier):
Page No: 424 - 427
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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