Visualization of Time‐Series Data in Parameter Space for Understanding Facial Dynamics

Tam, Gary K. L.; Fang, Hui; Aubrey, Andrew J.; Grant, P.W.; Rosin, Paul L.; Marshall, David; Chen, M.

doi:10.1111/j.1467-8659.2011.01939.x

Cited by 17 publications

(24 citation statements)

References 40 publications

(48 reference statements)

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“…In [57], Tam et al compared a visualization technique and a machine learning technique in generating a decision tree as a model for expression classification. The input to this model development exercise is a set of annotated videos, each of which records one of four expressions [anger, surprise, sadness, smile].…”

Section: Model-developmental Visualizationmentioning

confidence: 99%

What May Visualization Processes Optimize?

Chen

Golan

2016

IEEE Trans. Visual. Comput. Graphics

144

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Abstract-In this paper, we present an abstract model of visualization and inference processes and describe an information-theoretic measure for optimizing such processes. In order to obtain such an abstraction, we first examined six classes of workflows in data analysis and visualization, and identified four levels of typical visualization components, namely disseminative, observational, analytical and model-developmental visualization. We noticed a common phenomenon at different levels of visualization, that is, the transformation of data spaces (referred to as alphabets) usually corresponds to the reduction of maximal entropy along a workflow. Based on this observation, we establish an information-theoretic measure of cost-benefit ratio that may be used as a cost function for optimizing a data visualization process. To demonstrate the validity of this measure, we examined a number of successful visualization processes in the literature, and showed that the information-theoretic measure can mathematically explain the advantages of such processes over possible alternatives.

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Section: Model-developmental Visualizationmentioning

confidence: 99%

What May Visualization Processes Optimize?

Chen

Golan

2016

IEEE Trans. Visual. Comput. Graphics

144

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“…In PVA systems, feature selection has been supported by parallel coordinates [TFA*11, LKT*14], scatter plots [BvLBS11], and matrix views [KLTH10]. For example, INFUSE [KPB14] (Figure a) supports feature selection by comparing different measures in classification.…”

Section: Pva Pipelinementioning

confidence: 99%

The State‐of‐the‐Art in Predictive Visual Analytics

García

Hansen

et al. 2017

Computer Graphics Forum

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Predictive analytics embraces an extensive range of techniques including statistical modeling, machine learning, and data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline. Primary uses have been in data cleaning, exploratory analysis, and diagnostics. For example, scatterplots and bar charts are used to illustrate class distributions and responses. More recently, extensive visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent‐specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end‐users to understand and engage with the modeling process. In this state‐of‐the‐art report, we catalogue recent advances in the visualization community for supporting predictive analytics. First, we define the scope of predictive analytics discussed in this article and describe how visual analytics can support predictive analytics tasks in a predictive visual analytics (PVA) pipeline. We then survey the literature and categorize the research with respect to the proposed PVA pipeline. Systems and techniques are evaluated in terms of their supported interactions, and interactions specific to predictive analytics are discussed. We end this report with a discussion of challenges and opportunities for future research in predictive visual analytics.

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“…Since there are more than 300 facial feature measurements, it can be difficult to manually ascertain the reasons for this misclassification. In an attempt to visualise this aspect, a tool [49] which uses parallel coordinates and scatterplots to analyze these 13 measurements is employed. Parallel coordinates visualisation is known to be useful for identifying clusters, separation and outliers in high dimensional data space.…”

Section: Misclassified Sequence Analysismentioning

confidence: 99%

“…Due to space constraints, only a limited number of visualisations are shown. Such a large number of extreme measurements suggest that there may be problem extracting features for these sequences or that these sequences may even be incorrectly labelled [49]. In order to further investigate these sequences, this aspect is examined as part of the user study to determine whether humans can classify these sequences correctly.…”

Section: Misclassified Sequence Analysismentioning

confidence: 99%

Facial expression recognition in dynamic sequences: An integrated approach

Fang

Parthaláin

Aubrey

et al. 2014

Pattern Recognition

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Automatic facial expression analysis aims to analyse human facial expressions and classify them into discrete categories. Methods based on existing work are reliant on extracting information from video sequences and employ either some form of subjective thresholding of dynamic information or attempt to identify the particular individual frames in which the expected behaviour occurs. These methods are inefficient as they require either additional subjective information, tedious manual work or fail to take advantage of the information contained in the dynamic signature from facial movements for the task of expression recognition.In this paper, a novel framework is proposed for automatic facial expression analysis which extracts salient information from video sequences but does not rely on any subjective preprocessing or additional user-supplied information to select frames with peak expressions. The experimental framework demonstrates that the proposed method outperforms static expression recognition systems in terms of recognition rate. The approach does not rely on action units (AUs) and therefore, eliminates errors which are otherwise propagated to the final result due to incorrect initial identification of AUs. The proposed framework explores a parametric space of over 300 dimensions and is tested with six state-of-the-art machine learning techniques. Such robust and extensive experimentation provides an important foundation for the assessment of the performance for future work. A further contribution of the paper is offered in the form of a user study. This was conducted in order to investigate the correlation between human cognitive systems and the proposed framework for the understanding of human emotion classification and the reliability of public databases.

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Visualization of Time‐Series Data in Parameter Space for Understanding Facial Dynamics

Cited by 17 publications

References 40 publications

What May Visualization Processes Optimize?

What May Visualization Processes Optimize?

The State‐of‐the‐Art in Predictive Visual Analytics

Facial expression recognition in dynamic sequences: An integrated approach

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