A Discriminative Model for Age Invariant Face Recognition
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The discriminative model is developed to address face matching in the presence of age variation. In this approach, each face is represented by designing a densely sampled local feature description scheme, in which Scale Invariant Feature Transform (SIFT) and Multi-scale Local Binary Patterns (MLBP) serve as local descriptors. Since both SIFT-based local features and MLBP-based local features span a high- dimensional feature space, an algorithm called multi-feature discriminant analysis (MFDA) is used to process these two local feature spaces in a unified framework. The new proposed method is d...
The discriminative model is developed to address face matching in the presence of age variation. In this approach, each face is represented by designing a densely sampled local feature description scheme, in which Scale Invariant Feature Transform (SIFT) and Multi-scale Local Binary Patterns (MLBP) serve as local descriptors. Since both SIFT-based local features and MLBP-based local features span a high- dimensional feature space, an algorithm called multi-feature discriminant analysis (MFDA) is used to process these two local feature spaces in a unified framework. The new proposed method is discriminative model with multi-scale robust local binary pattern (MRLBP). MLBP is not so robust to the noise present in the image. The proposed method uses MRLBP which serve as the local descriptor in the discriminative model to enhance the performance.
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