Deep convolutional neural networks for Raman spectrum recognition: a unified solution.

Machine learning methods have found many applications in Raman spectroscopy, especially for the identification of chemical species. However, almost all of these methods require non-trivial preprocessing such as baseline correction and/or PCA as an essential step. Here we describe our unified solution for the identification of chemical species in which a convolutional neural network is trained to automatically identify substances according to their Raman spectrum without the need for preprocessing. We evaluated our approach using the RRUFF spectral database, comprising mineral sample data. Superior classification performance is demonstrated compared with other frequently used machine learning algorithms including the popular support vector machine method.

Breakthrough percepts of famous faces.

Recently, we showed that presenting salient names (i.e., a participant's first name) on the fringe of awareness (in rapid serial visual presentation, RSVP) breaks through into awareness, resulting in the generation of a P3, which (if concealed information is presented) could be used to differentiate between deceivers and nondeceivers. The aim of the present study was to explore whether face stimuli can be used in an ERP-based RSVP paradigm to infer recognition of broadly familiar faces. To do this, we explored whether famous faces differentially break into awareness when presented in RSVP and, importantly, whether ERPs can be used to detect these breakthrough events on an individual basis. Our findings provide evidence that famous faces are differentially perceived and processed by participants' brains as compared to novel (or unfamiliar) faces. EEG data revealed large differences in brain responses between these conditions.

Human facial beauty: current theories and methodologies.

This article examines current theories of beauty and describes recent progress in the ability to generate photorealistic faces using a computer. First, we describe a novel experimental tool, FacePrints, that allows a user to "evolve" an attractive face using a computer. We discuss the use of this program for research on human beauty and review the main experimental studies that have led to our current theoretical perspective: beauty is a product of sexual selection. Second, we outline major improvements to the FacePrints program and demonstrate the near photographic quality of facial composites that can be obtained by combining the FacePrints algorithm with a principal components analysis-based facial appearance model. The technical basis for a possible computer-planning system that could help the patient and surgeon define reasonable and desirable surgical outcomes is also outlined. Finally, we summarize the current state of the art and examine the issues that need to be addressed for developing the current program into a practical experimental and/or clinical tool.

Nonlinear, near photo-realistic caricatures using a parametric facial appearance model.

A mathematical model previously developed for use in computer vision applications is presented as an empirical model for face space. The term appearance space is used to distinguish this from previous models. Appearance space is a linear vector space that is dimensionally optimal, enables us to model and describe any human facial appearance, and possesses characteristics that are plausible for the representation of psychological face space. Randomly sampling from a multivariate distribution for a location in appearance space produces entirely plausible faces, and manipulation of a small set of defining parameters enables the automatic generation of photo-realistic caricatures. The appearance space model leads us to the new concept of nonlinear caricatures, and we show that the accepted linear method for caricature is only a special case of a more general paradigm. Nonlinear methods are also viable, and we present examples of photographic quality caricatures, using a number of different transformation functions. Results of a simple experiment are presented that suggest that nonlinear transformations can accurately capture key aspects of the caricature effect. Finally, we discuss the relationship between appearance space, caricature, and facial distinctiveness. On the basis of our new theoretical framework, we suggest an experimental approach that can yield new evidence for the plausibility of face space and its ability to explain processes of recognition.