Threshold Based Skin Color Classification

In this paper, we presented a new formula for skin classification. The proposed formula can overcome sensitivity to noise. Our approach was based multi-skin color Hue, Saturation, and Value color space and multi-level segmentation. Skin regions were extracted using three skin color classes, namely the Caucasoid, Mongolid and Nigroud. Moreover, in this formula, we adopted Gaussian-based weight k-NN algorithm for skin classification. The experiment result shows that the best result was achieved for Caucasoid class with 84.29 percent fmeasure

Emotion Recognition from Facial Expressions using Images with Pose, Illumination and Age Variation for Human-Computer/Robot Interaction

A technique for emotion recognition from facial expressions in images with simultaneous pose, illumination and age variation in real time is proposed in this paper. The basic emotions considered are anger, disgust, happy, surprise, and neutral. Feature vectors that were formed from images from the CMU-MultiPIE database for pose and illumination were used for training the classifier. For real-time implementation, Raspberry Pi II was used, which can be placed on a robot to recognize emotions in interactive real-time applications. The proposed method includes face detection using Viola Jones Haar cascade, Active Shape Model (ASM) for feature extraction, and AdaBoost for classification in real- time. Performance of the proposed method was validated in real time by testing with subjects from different age groups expressing basic emotions with varying pose and illumination. 96% recognition accuracy at an average time of 120 ms was obtained. The results are encouraging, as the proposed method gives better accuracy with higher speed compared to existing methods from the literature. The major contribution and strength of the proposed method lie in marking suitable feature points on the face, its speed and invariance to pose, illumination and age in real time

Retos y soluciones para una evaluación virtual más segura

En la formación semipresencial y virtual, la evaluación en línea representa uno de los mayores desafíos en lo que respecta a la identificación de estudiantes y la certificación en la autoría de sus trabajos. En la última década, está proliferando el uso de sistemas de seguridad, aunque su uso conlleva retos en el diseño de las actividades de evaluación. En este artículo se describe una experiencia docente de rediseño e implementación de un sistema de e-autenticación para incrementar la seguridad en la evaluación en línea. El sistema y la opinión de los diferentes actores se describe para dos asignaturas del Grado de Ingeniería Informática de la Universitat Oberta de Catalunya.E-assessment in blended and virtual education has become one of the major challenges for checking the identity of the students and the authorship of the work they perform. In the past decade, the use of secure systems is widespread. However, the design of e-assessment activities is seriously affected when those systems are integrated. In this paper, an experience is described based on the integration of an e-authentication system for a secure assessment. Considerations about the design and implementation of the activities are also shown. The system and the opinion of the actors in the teaching-learning process are shown from two courses of the Computer Science Bachelor in the Universitat Oberta de Catalunya.Este trabajo ha sido financiado por el proyecto H2020-ICT-2015/H2020-ICT-2015 TeSLA “An Adaptive Trust-based e-assessment System for Learning”, Number 688520

Non-Verbal Communication Analysis in Victim-Offender Mediations

In this paper we present a non-invasive ambient intelligence framework for the semi-automatic analysis of non-verbal communication applied to the restorative justice field. In particular, we propose the use of computer vision and social signal processing technologies in real scenarios of Victim-Offender Mediations, applying feature extraction techniques to multi-modal audio-RGB-depth data. We compute a set of behavioral indicators that define communicative cues from the fields of psychology and observational methodology. We test our methodology on data captured in real world Victim-Offender Mediation sessions in Catalonia in collaboration with the regional government. We define the ground truth based on expert opinions when annotating the observed social responses. Using different state-of-the-art binary classification approaches, our system achieves recognition accuracies of 86% when predicting satisfaction, and 79% when predicting both agreement and receptivity. Applying a regression strategy, we obtain a mean deviation for the predictions between 0.5 and 0.7 in the range [1-5] for the computed social signals.Comment: Please, find the supplementary video material at: http://sunai.uoc.edu/~vponcel/video/VOMSessionSample.mp