Neural Network through Face Recognition

A.K.Gupta ¹ , S.Gupta ²

1. Defence Research and Development Organisation, Delhi, India.
2. Association for Computing Machinery, Delhi, India.

Section:Research Paper, Product Type: Isroset-Journal
Vol.6 , Issue.2 , pp.38-40, Apr-2018

CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i2.3840

Online published on Apr 30, 2018

Copyright © A.K.Gupta, S.Gupta . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

View this paper at   Google Scholar | DPI Digital Library

XML View     PDF Download

How to Cite this Paper

591 Views    476 Downloads    200 Downloads

Abstract :
The aim is to utilise image processing to figure out lip movements and provide lice interaction with the system based on it. The multimodal HCI is displayed which enables a client to take a shot at a PC utilizing developments and motions made with the specific user’s mouth. Calculations for lip development and lip signal acknowledgement are introduced in points of interest. Client confront pictures are caught with a standard webcam. Face identification depends on a course of helped classifiers. Mouth position is utilized to track lip developments that enables a client to control a screen cursor. Three lip signals which are mouth opening, standing out tongue, and framing puckered lips respectively are perceived. An acknowledgment of lip is performed by simulated neural system.

Key-Words / Index Term :
human-computer interface, image processing; lip gestures, artificial neural network.

References :
[1]Aggarwal J. K.; Cai Q. (1999): Human Motion Analysis: A Review, CVIU(73), No. 3, pp. 428-440.
[2]Baecker R. M.; Grudin J.; Buxton W. A. S.; Greenberg S. (Eds.) (1995). Readings in human– computer interaction. Toward the Year 2000, 2nd edn. Morgan Kaufmann, San Francisco.
[3]Clausi D. A. (2002): An analysis of co-occurrence texture statistics as a function of grey-level quantization, Canadian Journal of Remote Sensing, 28(1), pp. 45-62.
[4]De Dios J.J.; Garcia, N. (2004): Fast face segmentation in component color space, Int. Conf. on Image Processing, ICIP, 1, pp. 191-194.
[5]Haralick R. M.; Shanmugam K.; Dinstein I. (1973): Textural Features for Image Classification, IEEE Transactions on Systems, Man, and Cybernetics, 3(6), pp. 610–621.
[6]Leung S.; Wang S.; Lau W. (2004): Lip image segmentation using fuzzy clustering incorporating an elliptic shape function, IEEE Transactions on Image Processing, 13(1), pp. 51-62.
[7]Lienhart R.; Maydt J. (2002): An Extended Set of Haar-like Features for Rapid Object Detection, IEEE ICIP, Vol. 1, pp. 900-903..
[8]Liewa W. C.; Leung S. H.; Lauw. H. (2000): Lip contour extraction using a deformable model, Proc. Int. Conf. Image Processing, Vancouver, Canada, 2, pp. 255–258.
[9] Moran L. E. L.; Pinto R.E. (2007): Automatic Extraction Of The Lips Shape Via Statistical Lips Modelling and Chromatic Feature, Electronics, Robotics and Automotive Mechanics Conference, CERMA, pp. 241-246.