Volume 25, Issue 2, July 2016, Pages 399–404
R. Karpagam1, P. Subbalakshmi2, and C. Balasubramanian3
1 Dept.of CSE, P.S.R. Rcollege of Engg For Women, Sivakasi, India
2 Asst.Prof Dept Of CSE, P.S.R. Rcollege of Engg For Women, Sivakasi, India
3 Prof. and head of Department of Computer Science and Engineering, P.S.R.Renasamy College of Engineering for women, Sivakasi, Tamilnadu, India
Original language: English
Copyright © 2016 ISSR Journals. 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.
In image processing is to identify the human faces in difficult to recognizing image analysis which has each day most applications. The main idea in the building of the detector is a learning classification built on ada-boost. The families of simple classifiers contain simple rectangular wavelets which are reminiscent of the Haar basis. Their ease and a new image representation called Integral Image allow a very quick computing of these Haar-like features. An assembly in cascade is introduced in order to reject quickly the easy to classify background regions and focus on the inflexible to classify windows. The structure of the SVM classifier allows a real-time implementation of the indicator. Some results on real world examples are presented. The detector yields good detection rates with frontal faces then the process can be easily adapted to other object detection tasks by changing the contents of the training dataset.
Author Keywords: Face recognition, Haar Wavelet Transform.
R. Karpagam1, P. Subbalakshmi2, and C. Balasubramanian3
1 Dept.of CSE, P.S.R. Rcollege of Engg For Women, Sivakasi, India
2 Asst.Prof Dept Of CSE, P.S.R. Rcollege of Engg For Women, Sivakasi, India
3 Prof. and head of Department of Computer Science and Engineering, P.S.R.Renasamy College of Engineering for women, Sivakasi, Tamilnadu, India
Original language: English
Copyright © 2016 ISSR Journals. 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.
Abstract
In image processing is to identify the human faces in difficult to recognizing image analysis which has each day most applications. The main idea in the building of the detector is a learning classification built on ada-boost. The families of simple classifiers contain simple rectangular wavelets which are reminiscent of the Haar basis. Their ease and a new image representation called Integral Image allow a very quick computing of these Haar-like features. An assembly in cascade is introduced in order to reject quickly the easy to classify background regions and focus on the inflexible to classify windows. The structure of the SVM classifier allows a real-time implementation of the indicator. Some results on real world examples are presented. The detector yields good detection rates with frontal faces then the process can be easily adapted to other object detection tasks by changing the contents of the training dataset.
Author Keywords: Face recognition, Haar Wavelet Transform.
How to Cite this Article
R. Karpagam, P. Subbalakshmi, and C. Balasubramanian, “FACE RECOGNITION USING HAAR WAVELET TRANSFORM,” International Journal of Innovation and Scientific Research, vol. 25, no. 2, pp. 399–404, July 2016.
