In this paper, we propose a novel classification method, called the nearest feature line (NFL), for face recognition. Any two feature points of the same class (person) are generalized by the feature line (FL) passing through the two points. The derived FL can capture more variations of face images than the original points and thus expands the capacity of the available database. The classification is based on the nearest distance from the query feature point to each FL. With a combined face database, the NFL error rate is about 43.7-65.4% of that of the standard eigenface method. Moreover, the NFL achieves the lowest error rate reported to date for the ORL face database.

Unusual dimers with wide interplanar separations, that is, very long bonds with d(D) approximately 3.05 A, are common to the spontaneous self-association of various organic pi-radicals in solution and in the crystalline solid state, independent of whether they are derived from negatively charged anion radicals of planar electron acceptors (TCNE-*, TCNQ-*, DDQ-*, CA-*), positively charged biphenylene cation-radical (OMB+*), or neutral phenalene radical (PHEN*). All dimeric species are characterized by intense absorption bands in the near-IR region that are diagnostic of the charge-transfer transitions previously identified with intermolecular associations of various electron-donor/acceptor dyads. The extensive delocalizations of a pair of pi-electrons accord with the sizable values of (i) the enthalpies (-Delta H(D)) and entropies (-Delta S(D)) of pi-dimerization measured by quantitative UV-vis/EPR spectroscopies and (ii) the electronic coupling element H(ab) evaluated from the strongly allowed optical transitions, irrespective of whether the diamagnetic dimeric species bear a double-negative charge as in (TCNE)(2)(2-), (TCNQ)(2)(2-), (DDQ)(2)(2-), (CA)(2)(2-) or a double-positive charge as in (OMB)(2)(2+) or are uncharged as in (PHEN)(2). These long-bonded dimers persist in solution as well as in the solid state and suffer only minor perturbations with Delta d(D) < 10% from extra-dimer forces that may be imposed by counterion electrostatics, crystal packing, and so forth. The characteristic optical transitions in such diamagnetic two-electron dimers are shown to be related to those in the corresponding paramagnetic one-electron pimers of the same pi-radicals with their parent acceptor, both in general accord with Mulliken theory.

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Abstract-In this paper, we propose a novel ensemble-based approach to boost performance of traditional Linear Discriminant Analysis (LDA)-based methods used in face recognition. The ensemble-based approach is based on the recently emerged technique known as "boosting." However, it is generally believed that boosting-like learning rules are not suited to a strong and stable learner such as LDA. To break the limitation, a novel weakness analysis theory is developed here. The theory attempts to boost a strong learner by increasing the diversity between the classifiers created by the learner, at the expense of decreasing their margins, so as to achieve a tradeoff suggested by recent boosting studies for a low generalization error. In addition, a novel distribution accounting for the pairwise class discriminant information is introduced for effective interaction between the booster and the LDA-based learner. The integration of all these methodologies proposed here leads to the novel ensemble-based discriminant learning approach, capable of taking advantage of both the boosting and LDA techniques. Promising experimental results obtained on various difficult face recognition scenarios demonstrate the effectiveness of the proposed approach. We believe that this work is especially beneficial in extending the boosting framework to accommodate general (strong/weak) learners.Index Terms-Boosting, face recognition (FR), linear discriminant analysis, machine learning, mixture of linear models, smallsample-size (SSS) problem, strong learner.

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Abstract-Techniques that can introduce low-dimensional feature representation with enhanced discriminatory power is of paramount importance in face recognition (FR) systems. It is well known that the distribution of face images, under a perceivable variation in viewpoint, illumination or facial expression, is highly nonlinear and complex. It is, therefore, not surprising that linear techniques, such as those based on principle component analysis (PCA) or linear discriminant analysis (LDA), cannot provide reliable and robust solutions to those FR problems with complex face variations. In this paper, we propose a kernel machine-based discriminant analysis method, which deals with the nonlinearity of the face patterns' distribution. The proposed method also effectively solves the so-called "small sample size" (SSS) problem, which exists in most FR tasks. The new algorithm has been tested, in terms of classification error rate performance, on the multiview UMIST face database. Results indicate that the proposed methodology is able to achieve excellent performance with only a very small set of features being used, and its error rate is approximately 34% and 48% of those of two other commonly used kernel FR approaches, the kernel-PCA (KPCA) and the generalized discriminant analysis (GDA), respectively. Index Terms-Face recognition (FR), kernel direct discriminant analysis (KDDA), linear discriminant analysis (LDA), principle component analysis (PCA), small sample size problem (SSS), kernel methods.