A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets
IEEE Computational Science & Engineering
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
An improved face recognition technique based on modular PCA approach
Pattern Recognition Letters
Handbook of Face Recognition
Neural Networks, Fuzzy Logic and Genetic Algorithms
Neural Networks, Fuzzy Logic and Genetic Algorithms
Wavelet Energy Entropy as a New Feature Extractor for Face Recognition
ICIG '07 Proceedings of the Fourth International Conference on Image and Graphics
Journal of Cognitive Neuroscience
Extraction of regions of interest from face images using cellular analysis
COMPUTE '08 Proceedings of the 1st Bangalore Annual Compute Conference
Face recognition using DCT coefficients selection
Proceedings of the 2008 ACM symposium on Applied computing
Eye localization for face matching: is it always useful and under what conditions?
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Face recognition using immune network based on principal component analysis
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Automatic facial expression recognition on a single 3D face by exploring shape deformation
MM '09 Proceedings of the 17th ACM international conference on Multimedia
A discriminated correlation classifier for face recognition
Proceedings of the 2010 ACM Symposium on Applied Computing
Face recognition using DCT and hierarchical RBF model
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Face segmentation using skin-color map in videophone applications
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, the problem of face recognition in still color images is addressed. An improved algorithm for face recognition is proposed here. The algorithm comprises of designing a feature vector which has discrete wavelet coefficients of the face and, a coefficient representing parameters of the face. Global features of the face are captured by wavelet coefficients and the local feature of the face is captured by facial parameter. The coefficients of the feature vector are used as inputs to the back-propagation architecture of the neural network. The network is trained for different images in the database. The proposed algorithm has been tested on various real images and its performance is found to be quite satisfactory when compared with the performance of conventional methods of face recognition such as the Eigen-face method.