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Face recognition still is a challenging task since face images may be affected by changes in the scene, such as in head pose, face expression, or illumination. In addition, face pattern representation often requires several dimensions, which poses additional challenges for face recognition. We propose a novel face recognition method based on projections of high-dimensional face image representations into lower dimensionality and highly discriminative spaces. This is achieved by a modified orthogonal locality preserving projection (OLPP) method that uses a customized locality definition scheme to preserve the face class structure in the lower dimensionality face feature space. The proposed method can work with sparse and dense face image representations (i.e., it can use subsets or all face image pixels) and tends to be robust to data outliers and noise. Besides, we introduce a sparse representation using interpolated landmarks, designed to preserve important details in high-resolution color face images (e.g., eyes), and compensate for uncertainties in landmark positioning during face image feature extraction. The face images are classified in this lower dimensionality feature space using a trained soft-margin support vector machine, so it performs better than the nearest neighbor rule used in the typical OLPP method. A set of experiments was designed to evaluate the proposed scheme under various conditions found in practice (such as changes in head pose, face expression, illumination, and in the presence of occlusion artifacts). The experimental results were obtained using five challenging public face databases (namely, Poznan University of Technology, Fundação Educacional Inaciana, Facial Recognition Technology, Yale, and Our Database of Faces). These experiments suggest that our sparse representation for high-resolution face color images, integrated to the proposed lower dimensionality feature space and classification scheme, tends to obtain higher accuracy values than those obtained using typical sparse and dense representations for the same face images in grayscale. To evaluate the generality of our lower dimensionality feature space and classification scheme, additional tests using full low-resolution grayscale face images were performed, as often used in face recognition (e.g., typical OLPP method). Our experiments suggest that the proposed approach can also provide higher accuracy values than comparable state-of-the-art methods available in the literature when using full low-resolution grayscale face images (i.e., dense representations).

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Visual monitoring of analogue meters is a critical task especially in isolated locations. Active shape models (ASMs) allow modelling of arbitrary shapes, even when they deform, by a shape adaptive technique. In this study, the authors describe a new ASM scheme for remote reading of panels with multiple analogue displays. Assuming a trained ASM for a given meter shape, the proposed scheme departs from a collection of landmark points detected in the input colour image, and makes meter readings by matching the meter pointer shape (position and orientation). The preliminary experimental results indicate that the authors' ASM scheme enhances the performance of automatic visual monitoring in contrast to human reading and other ASM approaches.

Active shape models is an adaptive shape-matching technique that has been used for locating facial features in images. However, when a number of features is extracted for each landmark point, distortions caused by noise or illumination, and the dimensionality of the final representation, have a negative impact in the performance of a classifier. In this paper, an evolutionary wrapper for selection of the most relevant set of features for face recognition is presented. The proposed strategy explores the space of multiple feasible selections using genetic algorithms. Experimental results show that the proposed approach allows to improve the classification performance in comparison with another enhanced method and a state of the art face recognition approach.