An independent evaluation of subspace facial recognition algorithms
Surajpal, Dhiresh Ramchander
In traversing the diverse field of biometric security and face recognition techniques, this investigation explores a rather rare comparative study of three of the most popular Appearance-based Face Recognition projection classes, these being the methodologies of Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA). Both the linear and kernel alternatives are investigated along with the four most widely accepted similarity measures of City Block (L1), Euclidean (L2), Cosine and the Mahalanobis metrics. Although comparisons between these classes can become fairly complex given the different task natures, the algorithm architectures and the distance metrics that must be taken into account, an important aspect of this study is the completely equal working conditions that are provided in order to facilitate fair and proper comparative levels of evaluation. In doing so, one is able to realise an independent study that significantly contributes to prior literary findings, either by verifying previous results, offering further insight into why certain conclusions were made or by providing a better understanding as to why certain claims should be disputed and under which conditions they may hold true. The experimental procedure examines ten 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. This investigation explores the weighted-sum approach, which by fusion at a matching score level, effectively harnesses the complimentary strengths of the component algorithms and in doing so highlights the improved performance levels that can be provided by hybrid implementations. In the process, by firstly exploring previous literature with respect to each other and secondly by relating the important findings of this paper to previous works one is also able to meet the primary objective in providing an amateur with a very insightful understanding of publicly available subspace techniques and their comparable application status within the environment of face recognition.
subspace , component , face recognition , PCA , LDA , ICA , hybrid