Abstract
Face recognition is one of the most popular biometric techniques for automatically identifying or verifying a person from a video or digital image. The face recognition accuracy can be affected by intraclass variations and interclass variations. A change in lighting condition is one of the intraclass variations. Preprocessing is an approach to normalize the intraclass variations of light varying image. Histogram equalization (HE) is one of the techniques to normalize the variations in illumination. But it is not suitable for well lighted images. Image quality based adaptive face recognition is used for well lighted face image recognition. The multiresolution property of wavelet transforms is used in face recognition to extract facial feature descriptors. Low and high frequency wavelet subbands are extracted and fusion of match scores from the subband is used to improve the recognition accuracy under varying lighting conditions. For face recognition, 2DPCA (2D Principle Component Analysis) method is used and can be verified with illumination variant face images. 2DPCA is based on 2D image matrices rather than 1D vector so the image matrix does not need to be transformed into a vector prior to feature extraction.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Sellahewa, H., Jassim, S.A.: Image-Quality-Based Adaptive Face Recognition. IEEE Transactions on Instrumentation and Measurement 59, 805–813 (2010)
Yang, J., Zhang, D., Frangi, A.F.: Two Dimensional PCA A New Approach to Appearance Based Face representation and recognition. IEEE Transaction on Pattern Analysis and Machine Intelligence 26, 131–137 (2004)
Jain, A.K., Ross, A., Prabhakar, S.: An Introduction to Biometric Recognition. IEEE Trans. On Circuits and Systems for Video Technology 14 (2004)
Chien, J.T.: Discriminant wavelet faces and nearest feature classifiers for face recognition. IEEE Transaction on Pattern Analysis and Machine Intelligence 24, 1644–1649 (2002)
Chen, W., Meng, J.E., Wu, S.: Illumination Compensation and Normalization for Robust Face Recognition Using Discrete Cosine Transform in Logarithm Domain. IEEE Transactions on Systems, Man, and Cybernetics 36, 458–466 (2006)
Zhang, T., Tang, Y.Y., Fang, B., Shang, Z.: Face Recognition under Varying Illumination Using Gradientfaces. IEEE Transactions on Image Processing 18, 2599–2606 (2009)
Li, S.Z., Chu, R.F., Liao, S.C., Zhang, L.: Illumination Invariant Face Recognition Using Near-Infrared Images. IEEE Transactions on Pattern Analysis And Machine Intelligence 29, 627–639 (2007)
Vázquez, H.M., Reyesand, E.G., Molleda, Y.C.: A New Image Division for LBP Method to Improve Face Recognition under Varying Lighting conditions. IEEE, Los Alamitos (2008)
Wang, H., Li, S.Z., Wang, Y.S.: Face Recognition under Varying Lighting Conditions Using Self Quotient Image. In: IEEE International Conference on Automatic Face and Gesture Recognition (2004)
Chen, T., Yin, W., Zhou, X.S.: Total Variation Models for Variable Lighting Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (2006)
Wang, Z., Bovik, A.C.: A Universal Image Quality Index. IEEE Signal Processing Letters 9, 81–84 (2002)
Shashua, A., Riklin-Raviv, T.: The Quotient Image: Class-Based Re-Rendering and Recognition with Varying Illuminations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 129–139 (2001)
Shan, S., Gao, W., Cao, B., Zhao, D.: Illumination normalization for robust face recognition against varying lighting conditions. In: Proc. IEEE Int. Workshop Anal. Model. Faces Gestures, pp. 157–164 (2003)
Ekenel, H.K., Sankur, B.: Multiresolution face recognition. J. Image and Vision Computing 23, 469–477 (2005)
Sellahewa, H., Jassim, S.A.: Illumination and expression invariant face recognition: Toward sample quality-based adaptive fusion. In: Proc. 2nd IEEE Int. Conf. Biometrics, Theory, Appl. Syst., pp. 1–6 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dilna, K.T., Senthilkumar, T.D. (2011). Quality Index Based Face Recognition under Varying Illumination Conditions. In: Abraham, A., Mauri, J.L., Buford, J.F., Suzuki, J., Thampi, S.M. (eds) Advances in Computing and Communications. ACC 2011. Communications in Computer and Information Science, vol 192. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22720-2_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-22720-2_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22719-6
Online ISBN: 978-3-642-22720-2
eBook Packages: Computer ScienceComputer Science (R0)