In this paper, a robust system for view-independent action unit intensity estimation is presented. Based on the theory of sparse coding, region-specific dictionaries are trained to approximate the characteristic of the individual action units. The system incorporates landmark detection, face alignment and contrast normalization to handle a large variety of different scenes. Coupled with head pose estimation, an ensemble of large margin classifiers is used to detect the individual action units. The experimental validation shows that our system is robust against pose variations and able to outperform the challenge baseline by more than 35%.

This paper introduces a new method to approximate Euclidean distances between points using block vectors in combination with the Hölder inequality. By defining lower bounds based on the proposed approximation, cluster algorithms can be considerably accelerated without loss of quality. In extensive experiments, we show a considerable reduction in terms of computational time in comparison to standard methods and the recently proposed Yinyang k-means. Additionally we show that the memory consumption of the presented clustering algorithm does not depend on the number of clusters, which makes the approach suitable for large scale problems.

Emotion recognition from facial expressions is a highly demanding task, especially in everyday life scenarios. Different sources of artifacts have to be considered in order to successfully extract the intended emotional nuances of the face. The exact and robust detection and orientation of faces impeded by occlusions, inhomogeneous lighting and fast movements is only one difficulty. Another one is the question of selecting suitable features for the application at hand. In the literature, a vast body of different visual features grouped into dynamic, spatial and textural families, has been proposed. These features exhibit different advantages/disadvantages over each other due to their inherent structure, and thus capture complementary information, which is a promising vantage point for fusion architectures. To combine different feature sets and exploit their respective advantages, an adaptive multilevel fusion architecture is proposed. The cascaded approach integrates information on different levels and time scales using artificial neural networks for adaptive weighting of propagated intermediate results. The performance of the proposed architecture is analysed on the GEMEP-FERA corpus as well as on a novel dataset obtained from an unconstrained, spontaneuous human-computer interaction scenario. The obtained performance is superior to single channels and basic fusion techniques.

Emotion recognition from speech is an important field of research in human-machine-interfaces, and has begun to influence everyday life by employment in different areas such as call centers or wearable companions in the form of smartphones. In the proposed classification architecture, different spectral, prosodic and the relatively novel voice quality features are extracted from the speech signals. These features are then used to represent long-term information of the speech, leading to utterance-wise suprasegmental features. The most promising of these features are selected using a forward-selection/backward-elimination algorithm with a novel long-term termination criterion for the selection. The overall system has been evaluated using recordings from the public Berlin emotion database. Utilizing the resulted features, a recognition rate of 88,97% has been achieved which surpasses the performance of humans on this database and is comparable to the state of the art performance on this dataset.

Reliable prediction of affective states in real world scenarios is very challenging and a significant amount of ongoing research is targeted towards improvement of existing systems. Major problems include the unreliability of labels, different realizations of the same affective states amongst different persons and in different modalities as well as the presence of sensor noise in the signals. This work presents a framework for adaptive fusion of input modalities with variable degrees of certainty on different levels. Using a strategy that starts with ensembles of weak learners, gradually, level by level, the discriminative power of the system is improved by adaptively weighting favorable decisions, while concurrently dismissing unfavorable ones. For the final decision fusion the proposed system leverages a trained Kalman filter. Besides its ability to deal with missing and uncertain values, in its nature, the Kalman filter is a time series predictor and thus a suitable choice to match input signals to a reference time series in the form of ground truth labels.

Systems for the recognition of psychological characteristics such as the emotional state in real world scenarios have to deal with several difficulties. Amongst those are unconstrained environments and uncertainties in one or several input channels. However a more crucial aspect is the content of the data itself. Psychological states are highly person-dependent and often even humans are not able to determine the correct state a person is in. A successful recognition system thus has to deal with data, that is not very discriminative and often simply misleading. In order to succeed, a critical view on features and decisions is essential to select only the most valuable ones. This work presents a comparison of a common multi classifier system approach based on state of the art features and a modified forward backward feature selection algorithm with a long term stopping criteria. The second approach takes also features of the voice quality family into account. Both approaches are based on the audio modality only. The dataset used in the challenge is an in between dataset of real world datasets which are still very hard to handle and over acted datasets which were famous in the past and today are well understood.

Abstract The high resolution of digital cameras has made single‐shot, single‐sensor acquisition of light fields feasible, though considerable design effort is still necessary in order to construct the necessary collection of optical elements for particular acquisition scenarios. This paper explores a pipeline for designing, fabricating and utilizing faceted mirror arrays which simplifies this task. The foundation of the pipeline is an interactive tool that automatically optimizes for mirror designs while exposing to the user a set of intuitive parameters for light field quality and manufacturing constraints. We investigate two manufacturing processes for automatic fabrication of the resulting designs: one is based on CNC milling, polishing, and plating of one solid work piece, while the other involves assembly of CNC‐cut mirror facets. We demonstrate results for refocusing in a macro photography scenario. In addition, we observe that traditional photographic parameters take novel roles in the faceted mirror array setup and discuss their influence.