A New Face Recognition Technique using Gabor Wavelet Transform and Back Propagation Network

This paper introduces a new face recognition technique using Gabor Wavelet transform and Back propagation network. Face recognition being regarded as a fundamental technology of biometrics has been applied to a variety of areas, including computer vision and pattern recognition. In this proposed approach, the features of the query face image and database face images have been extracted using Gabor transform and trained using BPN. The main objective of this proposed system is to develop an efficient face recognition system by improving the efficiency of the existing face recognition systems. The proposed system has been developed to provide efficiency in terms of retrieval accuracy and precision. The precision improved by 100 % and average recall rate of up to 97 % for the database of 100 images. The efficiency of the proposed system obtained as 100%.

[1]  Jing-Yu Yang,et al.  Face recognition based on the uncorrelated discriminant transformation , 2001, Pattern Recognit..

[2]  Ingemar J. Cox,et al.  Feature-based face recognition using mixture-distance , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Vijayan K. Asari,et al.  An improved face recognition technique based on modular PCA approach , 2004, Pattern Recognit. Lett..

[4]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[5]  I. Rock The Logic of Perception , 1983 .

[6]  Brian V. Funt,et al.  Is Machine Colour Constancy Good Enough? , 1998, ECCV.

[7]  Konstantinos N. Plataniotis,et al.  Face recognition using kernel direct discriminant analysis algorithms , 2003, IEEE Trans. Neural Networks.

[8]  K. Kim,et al.  Face recognition using kernel principal component analysis , 2002, IEEE Signal Process. Lett..

[9]  Venu Govindaraju,et al.  Holistic handwritten word recognition using temporal features derived from off-line images , 1996, Pattern Recognit. Lett..

[10]  Jordi Vitrià,et al.  Topological principal component analysis for face encoding and recognition , 2001, Pattern Recognit. Lett..

[11]  S. Zeki A vision of the brain , 1993 .

[12]  Li Bai,et al.  When eigenfaces are combined with wavelets , 2002, Knowl. Based Syst..

[13]  I. Biederman Recognition-by-components: a theory of human image understanding. , 1987, Psychological review.