Robust Face Recognition Using Enhanced Local Binary Pattern

Face recognition is an emerging research area in recognition of the people. A novel feature extraction technique was introduced for robust face recognition. Enhanced Local binary pattern (EnLBP) divided the image into sub regions. For each sub region, the salient features are extracted by obtaining the mean value of each sub region. In LBP, each pixel was replaced by applying LBP into each sub region. In this paper, the mean value of sub region was replaced for the sub region. It reduced the dimension of the image and extracts the salient information on each sub region. The extracted features are compared with similarity measures to recognize the person. EnLBP reduces the operation time and computational complexity of the system. The experimental results were carried out in the standard benchmark database LFW-a. The proposed system achieved a higher recognition rate than other local descriptors

Appliance identification using a histogram post-processing of 2D local binary patterns for smart grid applications

Identifying domestic appliances in the smart grid leads to a better power usage management and further helps in detecting appliance-level abnormalities. An efficient identification can be achieved only if a robust feature extraction scheme is developed with a high ability to discriminate between different appliances on the smart grid. Accordingly, we propose in this paper a novel method to extract electrical power signatures after transforming the power signal to 2D space, which has more encoding possibilities. Following, an improved local binary patterns (LBP) is proposed that relies on improving the discriminative ability of conventional LBP using a post-processing stage. A binarized eigenvalue map (BEVM) is extracted from the 2D power matrix and then used to post-process the generated LBP representation. Next, two histograms are constructed, namely up and down histograms, and are then concatenated to form the global histogram. A comprehensive performance evaluation is performed on two different datasets, namely the GREEND and WITHED, in which power data were collected at 1 Hz and 44000 Hz sampling rates, respectively. The obtained results revealed the superiority of the proposed LBP-BEVM based system in terms of the identification performance versus other 2D descriptors and existing identification frameworks.Comment: 8 pages, 10 figures and 5 table

Face Analysis Using Row and Correlation Based Local Directional Pattern

Face analysis, which includes face recognition and facial expression recognition, has been attempted by many researchers and gave ideal solutions. The problem is still active and challenging due to an increase in the complexity of the problem viz. due to poor lighting, face occlusion, low-resolution images, etc. Local pattern descriptor methods introduced to overcome these critical issues and improve the recognition rate. These methods extract the discriminant information from the local features of the face image for recognition. In this paper, the local descriptor based two methods, namely row-based local directional pattern and correlation-based local directional pattern proposed by extending an existing descriptor -- local directional pattern (LDP). Further, the two feature vectors obtained by these methods concatenated to form a hybrid descriptor. Experimentation has carried out on benchmark databases and results infer that the proposed hybrid descriptor outperforms the other descriptors in face analysis

Desarrollo de un sistema computacional para el análisis de procesos emocionales a través de las técnicas de reconocimiento facial y de potenciales relacionados a eventos

Objetivo: Establecer una metodología basada en las técnicas de reconocimiento facial y de potenciales relacionados a eventos para los procesos de orientación vocacional de universitarios. Metodología: Para la realización de este trabajo se consideró la metodología de desarrollo de software Proceso Unificado Racional (RUP), la cual incluyó las fases de Inicio, Elaboración, Construcción y Transición. Estas fases contemplan los análisis de requerimientos, diseño y construcción de la herramienta para el análisis de emociones, las pruebas con estudiantes, el análisis de pruebas y la entrega final del software. El sistema de análisis de emociones se construyó a través de Reconocimiento Facial de Emociones RFE (Affectiva), evaluación de Electroencefalografía EEG (Emotiv), sistema de gestión de protocolos, aplicación en formato digital de la prueba de Kuder y evaluación automática de las respuestas brindadas por los encuestados según área de interés. Resultados: Se consolidó una metodología basada en RFE y EEG para el análisis de emociones en procesos de orientación vocacional de estudiantes universitarios. Se automatizó la aplicación de la prueba de Kuder. Se realizó el desarrollo de la herramienta computacional para la presentación de protocolos, el seguimiento de emociones con RFE y EEG. Se validó la herramienta con 25 sujetos de prueba, los cuales respondieron la prueba de Kuder y fueron evaluados mediante la herramienta mientras observaban protocolos de estimulación asociados con sus áreas de interés y de no interés. Se analizaron los datos adquiridos y se encontró la efectividad de la herramienta encontrándose un porcentaje de afinidad con la prueba de orientación vocacional con un acierto cercano al 85%, especificidad del 87% y sensibilidad del 88%, resultados de valor para un ambiente de alta variabilidad. Conclusiones: Fue posible consolidar una metodología basada en el análisis de RFE y EEG para complementar las pruebas de orientación vocacional. Se utilizó como medio de referencia la prueba de Kuder que permitió validar la capacidad del método a la hora de identificar emociones al tiempo que se observan protocolos de estimulación asociados con áreas de desempeño vocacional. La metodología propuesta puede ser usada como complemento en los procesos de orientación vocacional y como una prueba rápida para encontrar afinidad entre el evaluado y las diferentes áreas de interés.Objective: To establish a methodology based on facial recognition techniques and event-related potentials for the vocational orientation processes of university students. Methodology: For the realization of this work, the Rational Unified Process (RUP) software development methodology was considered, which included the phases of Initiation, Elaboration, Construction and Transition. These phases contemplate the requirements analysis, design and construction of the emotion analysis tool, student testing, test analysis and final delivery of the software. The emotion analysis system was built through Facial Recognition of Emotions RFE (Affectiva), Electroencephalography EEG evaluation (Emotiv), protocol management system and automatic Kuder test evaluation. Results: A methodology based on RFE and EEG was consolidated for the analysis of emotions in vocational orientation processes of university students. The application of the Kuder test was automated. A computational tool was developed for the presentation of protocols, monitoring of emotions with RFE and EEG. The tool was validated with 25 test subjects, who responded to the Kuder test and were evaluated using the tool while observing stimulation protocols associated with their areas of interest and non-interest. The acquired data were analyzed and the effectiveness of the tool was found, finding a percentage of affinity with the vocational orientation test with a hit rate close to 85%, specificity of 87% and sensitivity of 88%, results of value for an environment of high variability.Conclusions: It was possible to consolidate a methodology based on RFE and EEG analysis to complement vocational orientation tests. The Kuder test was used as a reference medium, which allowed validating the ability of the method to identify emotions while observing stimulation protocols associated with areas of vocational performance. The proposed methodology can be used as a complement in vocational orientation processes and as a quick test to find affinity between the evaluated and the different areas of interest