Stress levels of student population in Costa Rica. Differences according to socioeconomic, academic achievement, educational level, and geographic location variables

En este trabajo se describen los niveles de estrés de la población estudiantil en función de las variables: nivel socioeconómico, rendimiento académico, nivel académico, zona geográfica y género. Para este estudio se aplicó una encuesta a una muestra aleatoria de 112 estudiantes de diferentes carreras de la sede central de la Universidad de Costa Rica. Los resultados mostraron que las mayores fuentes de estrés en los estudiantes son la académica y la financiera. También los estudiantes cuyo lugar de procedencia es diferente al de la sede central de la universidad, presentan mayores niveles de estrés. Los resultados mostraron que las mujeres tienden a manejar mayores niveles de estrés que los hombres.In this paper are described the stress levels of the student population about the variables: socioeconomic status, academic performance, academic level, geographical area and gender. For this purpose, a survey was administered to a randomized sample of 112 students of different careers from the main campus of the University of Costa Rica. Results showed that the major sources of stress in students are academic and financial pressures. Also, results showed that students whose place of origin is far from the main campus of the university have higher levels of stress and finally, women tend to handle higher levels of stress than men.Universidad de Costa Rica/[]/UCR/Costa RicaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigación en Estructuras Microscópicas (CIEMIC

3D Face Reconstruction from Light Field Images: A Model-free Approach

Reconstructing 3D facial geometry from a single RGB image has recently instigated wide research interest. However, it is still an ill-posed problem and most methods rely on prior models hence undermining the accuracy of the recovered 3D faces. In this paper, we exploit the Epipolar Plane Images (EPI) obtained from light field cameras and learn CNN models that recover horizontal and vertical 3D facial curves from the respective horizontal and vertical EPIs. Our 3D face reconstruction network (FaceLFnet) comprises a densely connected architecture to learn accurate 3D facial curves from low resolution EPIs. To train the proposed FaceLFnets from scratch, we synthesize photo-realistic light field images from 3D facial scans. The curve by curve 3D face estimation approach allows the networks to learn from only 14K images of 80 identities, which still comprises over 11 Million EPIs/curves. The estimated facial curves are merged into a single pointcloud to which a surface is fitted to get the final 3D face. Our method is model-free, requires only a few training samples to learn FaceLFnet and can reconstruct 3D faces with high accuracy from single light field images under varying poses, expressions and lighting conditions. Comparison on the BU-3DFE and BU-4DFE datasets show that our method reduces reconstruction errors by over 20% compared to recent state of the art

Recognising facial expressions in video sequences

We introduce a system that processes a sequence of images of a front-facing human face and recognises a set of facial expressions. We use an efficient appearance-based face tracker to locate the face in the image sequence and estimate the deformation of its non-rigid components. The tracker works in real-time. It is robust to strong illumination changes and factors out changes in appearance caused by illumination from changes due to face deformation. We adopt a model-based approach for facial expression recognition. In our model, an image of a face is represented by a point in a deformation space. The variability of the classes of images associated to facial expressions are represented by a set of samples which model a low-dimensional manifold in the space of deformations. We introduce a probabilistic procedure based on a nearest-neighbour approach to combine the information provided by the incoming image sequence with the prior information stored in the expression manifold in order to compute a posterior probability associated to a facial expression. In the experiments conducted we show that this system is able to work in an unconstrained environment with strong changes in illumination and face location. It achieves an 89\% recognition rate in a set of 333 sequences from the Cohn-Kanade data base

A framework for digital sunken relief generation based on 3D geometric models

Sunken relief is a special art form of sculpture whereby the depicted shapes are sunk into a given surface. This is traditionally created by laboriously carving materials such as stone. Sunken reliefs often utilize the engraved lines or strokes to strengthen the impressions of a 3D presence and to highlight the features which otherwise are unrevealed. In other types of reliefs, smooth surfaces and their shadows convey such information in a coherent manner. Existing methods for relief generation are focused on forming a smooth surface with a shallow depth which provides the presence of 3D figures. Such methods unfortunately do not help the art form of sunken reliefs as they omit the presence of feature lines. We propose a framework to produce sunken reliefs from a known 3D geometry, which transforms the 3D objects into three layers of input to incorporate the contour lines seamlessly with the smooth surfaces. The three input layers take the advantages of the geometric information and the visual cues to assist the relief generation. This framework alters existing techniques in line drawings and relief generation, and then combines them organically for this particular purpose

Weighted Fisher Discriminant Analysis in the Input and Feature Spaces

Fisher Discriminant Analysis (FDA) is a subspace learning method which minimizes and maximizes the intra- and inter-class scatters of data, respectively. Although, in FDA, all the pairs of classes are treated the same way, some classes are closer than the others. Weighted FDA assigns weights to the pairs of classes to address this shortcoming of FDA. In this paper, we propose a cosine-weighted FDA as well as an automatically weighted FDA in which weights are found automatically. We also propose a weighted FDA in the feature space to establish a weighted kernel FDA for both existing and newly proposed weights. Our experiments on the ORL face recognition dataset show the effectiveness of the proposed weighting schemes.Comment: Accepted (to appear) in International Conference on Image Analysis and Recognition (ICIAR) 2020, Springe

An Effective Approach to Pose Invariant 3D Face Recognition

Abstract. One critical challenge encountered by existing face recognition techniques lies in the difficulties of handling varying poses. In this paper, we propose a novel pose invariant 3D face recognition scheme to improve regular face recognition from two aspects. Firstly, we propose an effective geometry based alignment approach, which transforms a 3D face mesh model to a well-aligned 2D image. Secondly, we propose to represent the facial images by a Locality Preserving Sparse Coding (LPSC) algorithm, which is more effective than the regular sparse coding algorithm for face representation. We conducted a set of extensive experiments on both 2D and 3D face recognition, in which the encouraging results showed that the proposed scheme is more effective than the regular face recognition solutions.

Conditional Infomax Learning: An Integrated Framework for Feature Extraction and Fusion

Abstract. The paper introduces a new framework for feature learning in classification motivated by information theory. We first systematically study the information structure and present a novel perspective revealing the two key factors in information utilization: class-relevance and redun-dancy. We derive a new information decomposition model where a novel concept called class-relevant redundancy is introduced. Subsequently a new algorithm called Conditional Informative Feature Extraction is for-mulated, which maximizes the joint class-relevant information by explic-itly reducing the class-relevant redundancies among features. To address the computational difficulties in information-based optimization, we in-corporate Parzen window estimation into the discrete approximation of the objective function and propose a Local Active Region method which substantially increases the optimization efficiency. To effectively utilize the extracted feature set, we propose a Bayesian MAP formulation for feature fusion, which unifies Laplacian Sparse Prior and Multivariate Logistic Regression to learn a fusion rule with good generalization ca-pability. Realizing the inefficiency caused by separate treatment of the extraction stage and the fusion stage, we further develop an improved design of the framework to coordinate the two stages by introducing a feedback from the fusion stage to the extraction stage, which signifi-cantly enhances the learning efficiency. The results of the comparative experiments show remarkable improvements achieved by our framework.