Face Recognition System based on SURF and LDA Technique

Автор: Narpat A. Singh, Manoj B. Kumar, Manju C. Bala

Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa

Статья в выпуске: 2 vol.8, 2016 года.

Бесплатный доступ

In the past decade, Improve the quality in face recognition system is a challenge. It is a challenging problem and widely studied in the different type of imag-es to provide the best quality of faces in real life. These problems come due to illumination and pose effect due to light in gradient features. The improvement and optimization of human face recognition and detection is an important problem in the real life that can be handles to optimize the error rate, accuracy, peak signal to noise ratio, mean square error, and structural similarity Index. Now-a-days, there several methods are proposed to recognition face in different problem to optimize above parameters. There occur many invariant changes in hu-man faces due to the illumination and pose variations. In this paper we proposed a novel method in face recogni-tion to improve the quality parameters using speed up robust feature and linear discriminant analysis for opti-mize result. SURF is used for feature matching. In this paper, we use linear discriminant analysis for the edge dimensions reduction to live faces from our data-sets. The proposed method shows the better result as compare to the previous result on the basis of comparative analysis because our method show the better quality and better results in live images of face.

Еще

Image processing, Feature Extraction, Detection, Binarization, Discrete wavelet transform, Ga-bor filter, Speed up robust features, Linear Discriminant Analysis

Короткий адрес: https://sciup.org/15010791

IDR: 15010791

Список литературы Face Recognition System based on SURF and LDA Technique

  • G. Aneesh M U, Abhishek A K Masand and K Manikan-tana, “Optimal Feature Selection based on Image Pre-processing using Accelerated Binary Particle Swarm Op-timization for Enhanced Face Recognition”, ELSEVIER , 750-758 , 2011.
  • Matthew A. Turk and Alex and P.Pentland, “Face Recog-nition Using Eigenfaces”, IEEE, 586-591, 1991.
  • Priya Sisodia, Akhilesh Verma and Sachin Kansal, “Human Facial Expression Recognition using Gabor Filter Bank with Minimum Number of Feature Vectors ”, In-ternational Journal of Applied Information Systems (IJAIS), 9-13, July 2013.
  • Zhenhua Chai, Heydi Mendez-Vazquez, Ran He, Zhenan and Tienie Tan, “Explore semantic pixel sets based local patterns with information entropy for face recognition”, Springer, 2014.
  • J. Wang and Y. Tan, ``Hausdorff distance with k-nearest neighbors,'' in Advances in Swarm Intelligence. Berlin, Germany: Springer-Verlag, pp. 272-281, 2012.
  • Chandrappa D N and Ravishankar M ” Gabor Wavelets And Morphological Shared Weighted Neural Network Based Automatic Face Recognition”, Signal & Image Processing: An International Journal (SIPIJ), Vol.4, No.4, 61-70, August 2013.
  • Muhammad SHARIF, Adeeel KHALID, Mudassar RAZA and Sajjad MOHSIN, “Face Recognition using Gabor Filters”, Journal of Applied Computer Science & Mathe-matics, 53-55 , 2011.
  • Dong Hui and Han Dian Yuan, “Research of image Matching Algorithm Based on SURF features”, Interna-tional Conference on Computer Science and Information Processing (ICCSIP), 2012.
  • Shih-Wei Lin and Shih-Chieh Chen, “PSOLDA: A particle swarm optimization approach for enhancing classification accuracy rate of linear discriminated analysis“, Elsevier, 1008-1015, 2009.
  • David Bariena “Gabor wavelets in image processing”.
  • Gheorghita Ghinea, Rajkumar, Kannan, and Suresh Kan-naiyan, “Gradient-Orientation-Based PCA Subspace for Novel Face Recognition”, IEEE, 914-920, August 2014.
  • Ms. B. Saranya Bargavi and Ms C. Santhi, “Global and Local Facial Feature Extraction using Gabor Filters”, In-ternational Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 4, pp.1020-1023, April 2014 .
  • Kennedy, J. a Dong Hui and Han Dian Yuan “Research of Image Matching Algorithm Based on SURF Features”, International Conference on Computer Science and In-formation Processing (CSIP), 1140 – 1143, 2012.
  • F. Bashar, A. Khan, F. Ahmed, and H. Kabir, “Face recognition using similarity pattern of image directional edge response,'' Advance Electronic Computer Engineering, vol. 14, no. 1, pp. 69-76, 2014.
  • Lu, J, Plataniotis, K. N, and Venetsanopoulos and A. N, “Face recognition using LDA-based algorithms," IEEE Trans. on Neural Networks, 195-200, JANUARY 2003.
  • M. A. Turk and A. P. Pentland, “Face recognition using eigenfaces,'' in Proceeding IEEE Computer Socciety Conf. Comput. Vis. Pattern Recognition, pp. 586591, Jun. 1991
  • Ji-Sang Bae, Oh- young Lee and Jong-Ok Kim, “Image Interpolation Using Gabor Filter”, IEEE, 986-990, 2013.
  • T. Archana, Dr, T. Venugopal and M. Praneeth Kumar, “Face Recognition Methodologies Using Component Analysis: The Contemporary Affirmation of the Recent Literature”, Global Journal of Computer Science and Technology Graphics & Vision (GJCSTGV), Volume 12, Issue 13, 2012.
  • Sugreev Kaur and Rajesh Mehra, “High Speed And Area Efficient 2D DWT Processor Based Image Compression”, SIPIJ, pp. 22-31, vol. 1, no.2, dec 2010.
Еще
Статья научная