Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fractional-Step Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm
Genetic Programming and Evolvable Machines
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Artificial Evolution and Applications - Special issue on artificial evolution methods in the biological and biomedical sciences
PCA based immune networks for human face recognition
Applied Soft Computing
Face Recognition Using Nearest Feature Space Embedding
IEEE Transactions on Pattern Analysis and Machine Intelligence
A novel clonal selection algorithm for face detection
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Background learning for robust face recognition with PCA in the presence of clutter
IEEE Transactions on Image Processing
Orthogonal Laplacianfaces for Face Recognition
IEEE Transactions on Image Processing
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Face recognition algorithms often have to filter out the disturbances of some conditional factors such as facial pose, illumination, and expression (PIE). So an increasing number of researchers have been figuring out the best discrimi-nant transformation in the feature space of faces to improve the recognition performance. They have also proposed novel feature-matching algorithms to minimize the PIE effects. For example, Chen et al. designed a nearest feature space (NFS) embedding algorithm that outperformed the other algorithms for face recognition. By searching the most similar sample with immune learning, in this paper, a novel algorithm is proposed to filter out the disturbances of PIE for face recognition. The adaptive adjustment for filtering out the disturbance of PIE is designed with immune memory to maximize the success possibility for recognizing the faces. The clonal selection frame is used to search the most similar samples to the target face, and the selected antibodies are memorized as the candidates for the best solution or the second optimal solution. The proposed approach is evaluated on several benchmark databases and is compared with the NFS embedding algorithm. The experimental results show that the proposed approach outperforms the NFS embedding algorithm.