337 research outputs found

    Facial Expression Recognition

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    Human-computer interaction based on visual hand-gesture recognition using volumetric spatiograms of local binary patterns

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    A more natural, intuitive, user-friendly, and less intrusive Human–Computer interface for controlling an application by executing hand gestures is presented. For this purpose, a robust vision-based hand-gesture recognition system has been developed, and a new database has been created to test it. The system is divided into three stages: detection, tracking, and recognition. The detection stage searches in every frame of a video sequence potential hand poses using a binary Support Vector Machine classifier and Local Binary Patterns as feature vectors. These detections are employed as input of a tracker to generate a spatio-temporal trajectory of hand poses. Finally, the recognition stage segments a spatio-temporal volume of data using the obtained trajectories, and compute a video descriptor called Volumetric Spatiograms of Local Binary Patterns (VS-LBP), which is delivered to a bank of SVM classifiers to perform the gesture recognition. The VS-LBP is a novel video descriptor that constitutes one of the most important contributions of the paper, which is able to provide much richer spatio-temporal information than other existing approaches in the state of the art with a manageable computational cost. Excellent results have been obtained outperforming other approaches of the state of the art

    Markerless Analysis of Gait Patterns in the Parkinson's Disease

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    In the clinical praxis Gait Analysis constitutes one of the key tools for the diagnose and follow up of some pathologies. The conventional approach includes the approximation of the skeleton by the placement and detection of a set of markers, this procedure has some relevant drawbacks and can be better approached by a markerless strategy, where the dynamics of the body are estimated without the use of any artifact. The main goal of this thesis is to present some markerless approaches that allow the characterization of the human gait. For the analysis pathological gait, we focus on the Parkinson's Disease, a neurodegenerative disorder whose symptoms results in diculty to perform complex motor task among themwalking.Resumen. En la práctica clínica el análisis de marcha es una de las herramientas más importantes para el diagnostico y seguimiento de algunas patologías. Este análisis incluye la aproximación del esqueleto mediante marcadores colocados sobre el paciente. Debido a que este procedimiento tiene algunas desventajas, se han desarrollado aproximaciones sin marcadores para el análisis de marcha, estas intentan capturar la dinámica del movimiento del paciente prescindiendo de cualquier artefacto. El objetivo principal de esta tesis es presentar algunas aproximaciones sin marcadores al análisis para marcha patológica. La patología que analizamos es la enfermedad de parkinson, un desorden neurodegenerativo cuyos síntomas resultan en la creciente dificultad para realizar tareas motoras complejas entre ellas la marcha.Maestrí

    A spatial-temporal framework based on histogram of gradients and optical flow for facial expression recognition in video sequences

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    Facial expression causes different parts of the facial region to change over time and thus dynamic descriptors are inherently more suitable than static descriptors for recognising facial expressions. In this paper, we extend the spatial pyramid histogram of gradients to spatio-temporal domain to give 3-dimensional facial features and integrate them with dense optical flow to give a spatio-temporal descriptor which extracts both the spatial and dynamic motion information of facial expressions. A multi-class support vector machine based classifier with one-to-one strategy is used to recognise facial expressions. Experiments on the CK+ and MMI datasets using leave-one-out cross validation scheme demonstrate that the integrated framework achieves a better performance than using individual descriptor separately. Compared with six state of the art methods, the proposed framework demonstrates a superior performance

    Facial Expression Recognition Using New Feature Extraction Algorithm

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    This paper proposes a method for facial expression recognition. Facial feature vectors are generated from keypoint descriptors using Speeded-Up Robust Features. Each facial feature vector is then normalized and next the probability density function descriptor is generated. The distance between two probability density function descriptors is calculated using Kullback Leibler divergence. Mathematical equation is employed to select certain practicable probability density function descriptors for each grid, which are used as the initial classification. Subsequently, the corresponding weight of the class for each grid is determined using a weighted majority voting classifier. The class with the largest weight is output as the recognition result. The proposed method shows excellent performance when applied to the Japanese Female Facial Expression database

    Motion divergence fields for dynamic hand gesture recognition

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    Although it is in general difficult to track articulated hand motion, exemplar-based approaches provide a robust solution for hand gesture recognition. Presumably, a rich set of dynamic hand gestures are needed for a meaningful recognition system. How to build the visual representation for the motion patterns is the key for scalable recognition. We propose a novel representation based on the divergence map of the gestural motion field, which transforms motion patterns into spatial patterns. Given the motion divergence maps, we leverage modern image feature detectors to ex-tract salient spatial patterns, such as Maximum Stable Ex-tremal Regions (MSER). A local descriptor is extracted from each region to capture the local motion pattern. The de-scriptors from gesture exemplars are subsequently indexed using a pre-trained vocabulary tree. New gestures are then matched efficiently with the database gestures with a TF-IDF scheme. Our extensive experiments on a large hand gesture database with 10 categories and 1050 video sam-ples validate the efficacy of the extracted motion patterns for gesture recognition. The proposed approach achieves an overall recognition rate of 97.62%, while the average recognition time is only 34.53 ms. 1
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