ModDrop: adaptive multi-modal gesture recognition

We present a method for gesture detection and localisation based on multi-scale and multi-modal deep learning. Each visual modality captures spatial information at a particular spatial scale (such as motion of the upper body or a hand), and the whole system operates at three temporal scales. Key to our technique is a training strategy which exploits: i) careful initialization of individual modalities; and ii) gradual fusion involving random dropping of separate channels (dubbed ModDrop) for learning cross-modality correlations while preserving uniqueness of each modality-specific representation. We present experiments on the ChaLearn 2014 Looking at People Challenge gesture recognition track, in which we placed first out of 17 teams. Fusing multiple modalities at several spatial and temporal scales leads to a significant increase in recognition rates, allowing the model to compensate for errors of the individual classifiers as well as noise in the separate channels. Futhermore, the proposed ModDrop training technique ensures robustness of the classifier to missing signals in one or several channels to produce meaningful predictions from any number of available modalities. In addition, we demonstrate the applicability of the proposed fusion scheme to modalities of arbitrary nature by experiments on the same dataset augmented with audio.Comment: 14 pages, 7 figure

Machine learning for automatic analysis of affective behaviour

The automated analysis of affect has been gaining rapidly increasing attention by researchers over the past two decades, as it constitutes a fundamental step towards achieving next-generation computing technologies and integrating them into everyday life (e.g. via affect-aware, user-adaptive interfaces, medical imaging, health assessment, ambient intelligence etc.). The work presented in this thesis focuses on several fundamental problems manifesting in the course towards the achievement of reliable, accurate and robust affect sensing systems. In more detail, the motivation behind this work lies in recent developments in the field, namely (i) the creation of large, audiovisual databases for affect analysis in the so-called ''Big-Data`` era, along with (ii) the need to deploy systems under demanding, real-world conditions. These developments led to the requirement for the analysis of emotion expressions continuously in time, instead of merely processing static images, thus unveiling the wide range of temporal dynamics related to human behaviour to researchers. The latter entails another deviation from the traditional line of research in the field: instead of focusing on predicting posed, discrete basic emotions (happiness, surprise etc.), it became necessary to focus on spontaneous, naturalistic expressions captured under settings more proximal to real-world conditions, utilising more expressive emotion descriptions than a set of discrete labels. To this end, the main motivation of this thesis is to deal with challenges arising from the adoption of continuous dimensional emotion descriptions under naturalistic scenarios, considered to capture a much wider spectrum of expressive variability than basic emotions, and most importantly model emotional states which are commonly expressed by humans in their everyday life. In the first part of this thesis, we attempt to demystify the quite unexplored problem of predicting continuous emotional dimensions. This work is amongst the first to explore the problem of predicting emotion dimensions via multi-modal fusion, utilising facial expressions, auditory cues and shoulder gestures. A major contribution of the work presented in this thesis lies in proposing the utilisation of various relationships exhibited by emotion dimensions in order to improve the prediction accuracy of machine learning methods - an idea which has been taken on by other researchers in the field since. In order to experimentally evaluate this, we extend methods such as the Long Short-Term Memory Neural Networks (LSTM), the Relevance Vector Machine (RVM) and Canonical Correlation Analysis (CCA) in order to exploit output relationships in learning. As it is shown, this increases the accuracy of machine learning models applied to this task. The annotation of continuous dimensional emotions is a tedious task, highly prone to the influence of various types of noise. Performed real-time by several annotators (usually experts), the annotation process can be heavily biased by factors such as subjective interpretations of the emotional states observed, the inherent ambiguity of labels related to human behaviour, the varying reaction lags exhibited by each annotator as well as other factors such as input device noise and annotation errors. In effect, the annotations manifest a strong spatio-temporal annotator-specific bias. Failing to properly deal with annotation bias and noise leads to an inaccurate ground truth, and therefore to ill-generalisable machine learning models. This deems the proper fusion of multiple annotations, and the inference of a clean, corrected version of the ``ground truth'' as one of the most significant challenges in the area. A highly important contribution of this thesis lies in the introduction of Dynamic Probabilistic Canonical Correlation Analysis (DPCCA), a method aimed at fusing noisy continuous annotations. By adopting a private-shared space model, we isolate the individual characteristics that are annotator-specific and not shared, while most importantly we model the common, underlying annotation which is shared by annotators (i.e., the derived ground truth). By further learning temporal dynamics and incorporating a time-warping process, we are able to derive a clean version of the ground truth given multiple annotations, eliminating temporal discrepancies and other nuisances. The integration of the temporal alignment process within the proposed private-shared space model deems DPCCA suitable for the problem of temporally aligning human behaviour; that is, given temporally unsynchronised sequences (e.g., videos of two persons smiling), the goal is to generate the temporally synchronised sequences (e.g., the smile apex should co-occur in the videos). Temporal alignment is an important problem for many applications where multiple datasets need to be aligned in time. Furthermore, it is particularly suitable for the analysis of facial expressions, where the activation of facial muscles (Action Units) typically follows a set of predefined temporal phases. A highly challenging scenario is when the observations are perturbed by gross, non-Gaussian noise (e.g., occlusions), as is often the case when analysing data acquired under real-world conditions. To account for non-Gaussian noise, a robust variant of Canonical Correlation Analysis (RCCA) for robust fusion and temporal alignment is proposed. The model captures the shared, low-rank subspace of the observations, isolating the gross noise in a sparse noise term. RCCA is amongst the first robust variants of CCA proposed in literature, and as we show in related experiments outperforms other, state-of-the-art methods for related tasks such as the fusion of multiple modalities under gross noise. Beyond private-shared space models, Component Analysis (CA) is an integral component of most computer vision systems, particularly in terms of reducing the usually high-dimensional input spaces in a meaningful manner pertaining to the task-at-hand (e.g., prediction, clustering). A final, significant contribution of this thesis lies in proposing the first unifying framework for probabilistic component analysis. The proposed framework covers most well-known CA methods, such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Locality Preserving Projections (LPP) and Slow Feature Analysis (SFA), providing further theoretical insights into the workings of CA. Moreover, the proposed framework is highly flexible, enabling novel CA methods to be generated by simply manipulating the connectivity of latent variables (i.e. the latent neighbourhood). As shown experimentally, methods derived via the proposed framework outperform other equivalents in several problems related to affect sensing and facial expression analysis, while providing advantages such as reduced complexity and explicit variance modelling.Open Acces

Human activity recognition using a wearable camera

Tesi en modalitat cotutela Universitat Politècnica de Catalunya i Queen Mary, University of London. This PhD Thesis has been developed in the framework of, and according to, the rules of the Erasmus Mundus Joint Doctorate on Interactive and Cognitive Environments EMJD ICE [FPA n° 2010-0012]Advances in wearable technologies are facilitating the understanding of human activities using first-person vision (FPV) for a wide range of assistive applications. In this thesis, we propose robust multiple motion features for human activity recognition from first­ person videos. The proposed features encode discriminant characteristics form magnitude, direction and dynamics of motion estimated using optical flow. M:>reover, we design novel virtual-inertial features from video, without using the actual inertial sensor, from the movement of intensity centroid across frames. Results on multiple datasets demonstrate that centroid-based inertial features improve the recognition performance of grid-based features. Moreover, we propose a multi-layer modelling framework that encodes hierarchical and temporal relationships among activities. The first layer operates on groups of features that effectively encode motion dynamics and temporal variaitons of intra-frame appearance descriptors of activities with a hierarchical topology. The second layer exploits the temporal context by weighting the outputs of the hierarchy during modelling. In addition, a post-decoding smoothing technique utilises decisions on past samples based on the confidence of the current sample. We validate the proposed framework with several classi fiers, and the temporal modelling is shown to improve recognition performance. We also investigate the use of deep networks to simplify the feature engineering from first-person videos. We propose a stacking of spectrograms to represent short-term global motions that contains a frequency-time representation of multiplemotion components. This enables us to apply 2D convolutions to extract/learn motion features. We employ long short-term memory recurrent network to encode long-term temporal dependency among activiites. Furthermore, we apply cross-domain knowledge transfer between inertial­ based and vision-based approaches for egocentric activity recognition. We propose sparsity weightedcombination of information from different motion modalities and/or streams . Results show that the proposed approach performs competitively with existing deep frameworks, moreover, with reduced complexity.Los avances en tecnologías wearables facilitan la comprensión de actividades humanas utilizando cuando se usan videos grabados en primera persona para una amplia gama de aplicaciones. En esta tesis, proponemos características robustas de movimiento para el reconocimiento de actividades humana a partir de videos en primera persona. Las características propuestas codifican características discriminativas estimadas a partir de optical flow como magnitud, dirección y dinámica de movimiento. Además, diseñamos nuevas características de inercia virtual a partir de video, sin usar sensores inerciales, utilizando el movimiento del centroide de intensidad a través de los fotogramas. Los resultados obtenidos en múltiples bases de datos demuestran que las características inerciales basadas en centroides mejoran el rendimiento de reconocimiento en comparación con grid-based características. Además, proponemos un algoritmo multicapa que codifica las relaciones jerárquicas y temporales entre actividades. La primera capa opera en grupos de características que codifican eficazmente las dinámicas del movimiento y las variaciones temporales de características de apariencia entre múltiples fotogramas utilizando una jerarquía. La segunda capa aprovecha el contexto temporal ponderando las salidas de la jerarquía durante el modelado. Además, diseñamos una técnica de postprocesado para filtrar las decisiones utilizando estimaciones pasadas y la confianza de la estimación actual. Validamos el algoritmo propuesto utilizando varios clasificadores. El modelado temporal muestra una mejora del rendimiento en el reconocimiento de actividades. También investigamos el uso de redes profundas (deep networks) para simplificar el diseño manual de características a partir de videos en primera persona. Proponemos apilar espectrogramas para representar movimientos globales a corto plazo. Estos espectrogramas contienen una representación espaciotemporal de múltiples componentes de movimiento. Esto nos permite aplicar convoluciones bidimensionales para aprender funciones de movimiento. Empleamos long short-term memory recurrent networks para codificar la dependencia temporal a largo plazo entre las actividades. Además, aplicamos transferencia de conocimiento entre diferentes dominios (cross-domain knowledge) entre enfoques inerciales y basados en la visión para el reconocimiento de la actividad en primera persona. Proponemos una combinación ponderada de información de diferentes modalidades de movimiento y/o secuencias. Los resultados muestran que el algoritmo propuesto obtiene resultados competitivos en comparación con existentes algoritmos basados en deep learning, a la vez que se reduce la complejidad.Postprint (published version

Human activity recognition using a wearable camera

Advances in wearable technologies are facilitating the understanding of human activities using first-person vision (FPV) for a wide range of assistive applications. In this thesis, we propose robust multiple motion features for human activity recognition from first­ person videos. The proposed features encode discriminant characteristics form magnitude, direction and dynamics of motion estimated using optical flow. M:>reover, we design novel virtual-inertial features from video, without using the actual inertial sensor, from the movement of intensity centroid across frames. Results on multiple datasets demonstrate that centroid-based inertial features improve the recognition performance of grid-based features. Moreover, we propose a multi-layer modelling framework that encodes hierarchical and temporal relationships among activities. The first layer operates on groups of features that effectively encode motion dynamics and temporal variaitons of intra-frame appearance descriptors of activities with a hierarchical topology. The second layer exploits the temporal context by weighting the outputs of the hierarchy during modelling. In addition, a post-decoding smoothing technique utilises decisions on past samples based on the confidence of the current sample. We validate the proposed framework with several classi fiers, and the temporal modelling is shown to improve recognition performance. We also investigate the use of deep networks to simplify the feature engineering from first-person videos. We propose a stacking of spectrograms to represent short-term global motions that contains a frequency-time representation of multiplemotion components. This enables us to apply 2D convolutions to extract/learn motion features. We employ long short-term memory recurrent network to encode long-term temporal dependency among activiites. Furthermore, we apply cross-domain knowledge transfer between inertial­ based and vision-based approaches for egocentric activity recognition. We propose sparsity weightedcombination of information from different motion modalities and/or streams . Results show that the proposed approach performs competitively with existing deep frameworks, moreover, with reduced complexity.Los avances en tecnologías wearables facilitan la comprensión de actividades humanas utilizando cuando se usan videos grabados en primera persona para una amplia gama de aplicaciones. En esta tesis, proponemos características robustas de movimiento para el reconocimiento de actividades humana a partir de videos en primera persona. Las características propuestas codifican características discriminativas estimadas a partir de optical flow como magnitud, dirección y dinámica de movimiento. Además, diseñamos nuevas características de inercia virtual a partir de video, sin usar sensores inerciales, utilizando el movimiento del centroide de intensidad a través de los fotogramas. Los resultados obtenidos en múltiples bases de datos demuestran que las características inerciales basadas en centroides mejoran el rendimiento de reconocimiento en comparación con grid-based características. Además, proponemos un algoritmo multicapa que codifica las relaciones jerárquicas y temporales entre actividades. La primera capa opera en grupos de características que codifican eficazmente las dinámicas del movimiento y las variaciones temporales de características de apariencia entre múltiples fotogramas utilizando una jerarquía. La segunda capa aprovecha el contexto temporal ponderando las salidas de la jerarquía durante el modelado. Además, diseñamos una técnica de postprocesado para filtrar las decisiones utilizando estimaciones pasadas y la confianza de la estimación actual. Validamos el algoritmo propuesto utilizando varios clasificadores. El modelado temporal muestra una mejora del rendimiento en el reconocimiento de actividades. También investigamos el uso de redes profundas (deep networks) para simplificar el diseño manual de características a partir de videos en primera persona. Proponemos apilar espectrogramas para representar movimientos globales a corto plazo. Estos espectrogramas contienen una representación espaciotemporal de múltiples componentes de movimiento. Esto nos permite aplicar convoluciones bidimensionales para aprender funciones de movimiento. Empleamos long short-term memory recurrent networks para codificar la dependencia temporal a largo plazo entre las actividades. Además, aplicamos transferencia de conocimiento entre diferentes dominios (cross-domain knowledge) entre enfoques inerciales y basados en la visión para el reconocimiento de la actividad en primera persona. Proponemos una combinación ponderada de información de diferentes modalidades de movimiento y/o secuencias. Los resultados muestran que el algoritmo propuesto obtiene resultados competitivos en comparación con existentes algoritmos basados en deep learning, a la vez que se reduce la complejidad

Human activity recognition using a wearable camera

Advances in wearable technologies are facilitating the understanding of human activities using first-person vision (FPV) for a wide range of assistive applications. In this thesis, we propose robust multiple motion features for human activity recognition from first­ person videos. The proposed features encode discriminant characteristics form magnitude, direction and dynamics of motion estimated using optical flow. M:>reover, we design novel virtual-inertial features from video, without using the actual inertial sensor, from the movement of intensity centroid across frames. Results on multiple datasets demonstrate that centroid-based inertial features improve the recognition performance of grid-based features. Moreover, we propose a multi-layer modelling framework that encodes hierarchical and temporal relationships among activities. The first layer operates on groups of features that effectively encode motion dynamics and temporal variaitons of intra-frame appearance descriptors of activities with a hierarchical topology. The second layer exploits the temporal context by weighting the outputs of the hierarchy during modelling. In addition, a post-decoding smoothing technique utilises decisions on past samples based on the confidence of the current sample. We validate the proposed framework with several classi fiers, and the temporal modelling is shown to improve recognition performance. We also investigate the use of deep networks to simplify the feature engineering from first-person videos. We propose a stacking of spectrograms to represent short-term global motions that contains a frequency-time representation of multiplemotion components. This enables us to apply 2D convolutions to extract/learn motion features. We employ long short-term memory recurrent network to encode long-term temporal dependency among activiites. Furthermore, we apply cross-domain knowledge transfer between inertial­ based and vision-based approaches for egocentric activity recognition. We propose sparsity weightedcombination of information from different motion modalities and/or streams . Results show that the proposed approach performs competitively with existing deep frameworks, moreover, with reduced complexity.Los avances en tecnologías wearables facilitan la comprensión de actividades humanas utilizando cuando se usan videos grabados en primera persona para una amplia gama de aplicaciones. En esta tesis, proponemos características robustas de movimiento para el reconocimiento de actividades humana a partir de videos en primera persona. Las características propuestas codifican características discriminativas estimadas a partir de optical flow como magnitud, dirección y dinámica de movimiento. Además, diseñamos nuevas características de inercia virtual a partir de video, sin usar sensores inerciales, utilizando el movimiento del centroide de intensidad a través de los fotogramas. Los resultados obtenidos en múltiples bases de datos demuestran que las características inerciales basadas en centroides mejoran el rendimiento de reconocimiento en comparación con grid-based características. Además, proponemos un algoritmo multicapa que codifica las relaciones jerárquicas y temporales entre actividades. La primera capa opera en grupos de características que codifican eficazmente las dinámicas del movimiento y las variaciones temporales de características de apariencia entre múltiples fotogramas utilizando una jerarquía. La segunda capa aprovecha el contexto temporal ponderando las salidas de la jerarquía durante el modelado. Además, diseñamos una técnica de postprocesado para filtrar las decisiones utilizando estimaciones pasadas y la confianza de la estimación actual. Validamos el algoritmo propuesto utilizando varios clasificadores. El modelado temporal muestra una mejora del rendimiento en el reconocimiento de actividades. También investigamos el uso de redes profundas (deep networks) para simplificar el diseño manual de características a partir de videos en primera persona. Proponemos apilar espectrogramas para representar movimientos globales a corto plazo. Estos espectrogramas contienen una representación espaciotemporal de múltiples componentes de movimiento. Esto nos permite aplicar convoluciones bidimensionales para aprender funciones de movimiento. Empleamos long short-term memory recurrent networks para codificar la dependencia temporal a largo plazo entre las actividades. Además, aplicamos transferencia de conocimiento entre diferentes dominios (cross-domain knowledge) entre enfoques inerciales y basados en la visión para el reconocimiento de la actividad en primera persona. Proponemos una combinación ponderada de información de diferentes modalidades de movimiento y/o secuencias. Los resultados muestran que el algoritmo propuesto obtiene resultados competitivos en comparación con existentes algoritmos basados en deep learning, a la vez que se reduce la complejidad