Independent Evaluation of Subspace Face Recognition Algorithms
نویسندگان
چکیده
This investigation explores a comparative study of both the linear and kernel implementations of three of the most popular Appearance-based Face Recognition projection classes. These are Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA). The experimental procedure provides a platform of equal working conditions and examines algorithms in the categories of expression, illumination, occlusion and temporal delay. The results are then evaluated based on a sequential combination of assessment tools that facilitate both intuitive and statistical decisiveness among the intra and inter-class comparisons. In a bid to boost the overall efficiency and accuracy levels of the identification system, the ‘best’ categorical algorithms are then incorporated into a hybrid methodology, where the advantageous effects of fusion strategies are considered. Index Terms – CMS, Hybrid, McNemar, Rank, Subspace
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