Linguistics and face recognition
We describe in this paper a novel biometric methodology for face recognition suitable to address pose, illumination, and expression (PIE) image variability, temporal change, flexible matching, and last but not least occlusion and disguise that are usually referred to as denial and deception. The adverse conditions listed above affect the scope and performance of biometric analysis vis--vis both training and testing. The conceptual framework proposed here draws support from discriminative methods using likelihood ratios. At the conceptual level it links forensics and biometrics, while at the implementation level it links the Bayesian framework and statistical learning theory. As many of the concerns listed usually affect only parts of the face, a non-parametric recognition-by-part approach is advanced here for the purpose of reliable face recognition. Recognition-by-parts facilitates authentication because it does not seek for explicit invariance. Instead, it handles variability using component-based configurations that are flexible enough to compensate among others for limited pose changes, if any, and limited occlusion and disguise. The recognition-by-parts approach proposed here supports incremental and progressive processing. It is similar in nature to modern linguistics and practical intelligence with the emphasis on semantics and pragmatics. Layered categorization starts with face detection using implicit rather than explicit segmentation. It proceeds with face authentication that involves feature selection of local patch instances including dimensionality reduction, exemplar-based clustering of patches into parts, and data fusion for matching using boosting driven by parts that play the role of weak learners. The implementation, driven by transduction, employs proximity and typicality (ranking) realized using strangeness and random deficiency p-values, respectively. The feasibility and reliability of the proposed architecture has been validated using FERET and FRGC data. The paper concludes with suggestions for augmenting and enhancing the scope and utility of the recognition-by-parts architecture.
linguistics face recognition recognition biometrics occlusion likelihood ratio forensics recognition-by-parts authentication segmentation feature selection clustering data fusion boosting transduction typicality ranking strangeness random deficiency feret frgc cross-validation ica k nearest neighbor margin mdl multimodal integration neymanpearson p-values parsing sift surveillance