Abstract
In this paper, face recognition technique using real Dual Tree Discrete Wavelet Transform (DT-DWT) is proposed. This approach is based on local appearance feature extraction using directional multiresolution decomposition offered by real DT-DWT. It provides a local multiscale description of images with good directional selectivity, effective edge representation and invariance to shifts and in-plane rotations. The real DT-DWT is less redundant and computationally efficient. The fusion of local coefficients of detail subbands are used to extract the facial features using feature variance. The features are used for classification process using Mahalanobis distance. The technique is validated using 9 pose and 2 pose per person of ORL face dataset and 2 pose per person of YALE face dataset. The experimental results outperform traditional methods like PCA, LDA and DCT normalization.
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Maheshkar, V., Kamble, S., Agarwal, S., Agarwal, V.K. (2012). Real Dual Tree Based Feature Variance for Face Recognition. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 131. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0491-6_58
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DOI: https://doi.org/10.1007/978-81-322-0491-6_58
Publisher Name: Springer, New Delhi
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