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The study aims to answer this research question: How to train a machine to recognize and better understand hidden human emotions via body gestures like a trained expert? Specifically, we break down this question into several sub-problems with corresponding solutions: (1) Unlike the Action Units (AU) in facial action coding system (FACS) (Ekman 1997), a common standard is absent for body gesture-based emotion measurement. The lack of this empirical guidance leaves even psychological professionals without complete agreement on annotating bodily expressions (Luo et al. 2020). Thus, we present a novel dataset of MGs, which was collected under objective proxy tasks to stimulate two states of emotional stress. (2) The high heterogeneity in the same gesture class makes the classification of MG much more complicated than ordinary gestures. Thus, we provide various state-of-the-art models from recent top computer vision venues to demonstrate the benchmark. (3) Accurately spotting MGs from unconstrained streams is another highly challenging task, as MGs are subtle and rapid body movements that can easily be submerged in other unrelated body movements. To this end, we propose a novel online detecting method that has a parameter-free attention mechanism to differentiate MGs from non-MGs adaptively. (4) The conventional paradigm that imposes each gesture with an emotional state does not resemble real-world scenarios, we explore a new paradigm that achieves emotional understanding by holistically considering all the MGs.
A hidden Markov model (HMM) recurrent network for online MG recognition is proposed with a novel parameter-free attention mechanism. The method is intensively validated on three online gesture recognition datasets with competitive performances.
We proposed a novel, psychology-based and reliable paradigm for body gesture-based emotion understanding with computer vision methods. To our knowledge, our effort is the first to interpret hidden emotion states via MGs, with both quantitative investigations of human body behaviors and machine vision technologies. A related spontaneous micro-gesture dataset towards hidden emotion understanding is collected. A comprehensive static analysis is performed with significant findings for MGs and emotional body gestures. Benchmarks for MG classification, MG online recognition, and body gesture-based emotional stress state recognition are provided with state-of-the-art models. Our proposed AED-BiLSTM framework can efficiently provide a more robust correction to the prior with a parameter-free mechanism. Experiments show that AED-BiLSTM can efficiently improve online recognition performance in a practice closer to a real-world setting. Moreover, a graph-based network is proposed for the MG pattern representations to better analyze the emotional states.
In the West there exists a system which is historically inspired by the principles of the liberal capitalism which developed with industrialization during the last century. In the East there exists a system inspired by the Marxist collectivism which sprang from an interpretation of the condition of the proletarian classes made in the light of a particular reading of history. Each of the two ideologies, on the basis of two very different visions of man and of his freedom and social role, has proposed and still promotes, on the economic level, antithetical forms of the organization of labor and of the structures of ownership, especially with regard to the so-called means of production. 041b061a72