2021
DOI: 10.48550/arxiv.2107.03886
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Causal affect prediction model using a facial image sequence

Geesung Oh,
Euiseok Jeong,
Sejoon Lim

Abstract: Among human affective behavior research, facial expression recognition research is improving in performance along with the development of deep learning. However, for improved performance, not only past images but also future images should be used along with corresponding facial images, but there are obstacles to the application of this technique to real-time environments. In this paper, we propose the causal affect prediction network (CAPNet), which uses only past facial images to predict corresponding affecti… Show more

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Cited by 3 publications
(4 citation statements)
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“…This adaptive threshold is used to mark the predictions on input images as confident and non-confident ones. CE is cross entropy loss 5 and is used on the confident predictions of weak and strong augmentations, KL is symmetric kl-divergence 8 between the probability distributions of non-confident weak and strong predictions and the remaining losses are as in MFAR.…”
Section: Mfar : Multi-task Facial Affect Recognitionmentioning
confidence: 99%
See 1 more Smart Citation
“…This adaptive threshold is used to mark the predictions on input images as confident and non-confident ones. CE is cross entropy loss 5 and is used on the confident predictions of weak and strong augmentations, KL is symmetric kl-divergence 8 between the probability distributions of non-confident weak and strong predictions and the remaining losses are as in MFAR.…”
Section: Mfar : Multi-task Facial Affect Recognitionmentioning
confidence: 99%
“…Automatic affect recognition is currently an active area of research and has applications in many areas such as education, gaming, software development, auto motives [1,2], medical care, etc. Many works have dealt with Valence-arousal estimation [3,4,5,6,7], action unit detection [8,9,10], expression classification [] tasks individually. [11] introduced a framework which uses only static images and Multi-Task-Learning (MTL) to learn categorical representations and use them to estimate dimensional representation, but is limited to AffectNet data set [12].…”
Section: Introductionmentioning
confidence: 99%
“…The third ABAW Competition, to be held in conjunction with the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2022 is a continuation of the first [24] and second [32] ABAW Competitions held in conjunction with the IEEE Conference on Face and Gesture Recognition (IEEE FG) 2021 and with the International Conference on Computer Vision (ICCV) 2022, respectively, which targeted dimensional (in terms of valence and arousal) [2][3][4]8,9,11,21,35,39,47,48,50,[54][55][56], categorical (in terms of the basic expressions) [12,15,16,33,36,37,51] and facial action unit analysis and recognition [7,19,20,25,26,40,44,47]. The third ABAW Competition contains four Challenges, which are based on the same in-the-wild database, (i) the uni-task Valence-Arousal Estimation Challenge; (ii) the uni-task Expression Classification Challenge (for the 6 basic expressions plus the neutral state plus the 'other' category that denotes expressions/affective states other than the 6 basic ones); (iii) the uni-task Action Unit Detection Challenge (for 12 action units); (iv) the Multi-Task Learning Challenge (for joint learning and predicting of valence-arousal, 8 expressions -6 basic plus neutral plus 'other'-and 12 action units).…”
Section: Introductionmentioning
confidence: 99%
“…Importantly, E-RNN can be combined with various state-of-the-art RNN techniques such as multilayer (Parlos et al, 1994), dropout (Srivastava et al, 2014), LSTM (Hochreiter & Schmidhuber, 1997), and GRU (Chung et al, 2015) and leads to performance gains, without the need for any additional assumptions. Entangled Recurrent Neural Networks have been used in multiple domains such as face image sequencing (Oh et al, 2021), load forecasting (Sriram et al, 2018), and other power system applications (Sriram, 2020). However, as aforementioned, to the best of the authors' knowledge, E-RNNs have not been used in the domain of OR, especially in energy demand forecasting.…”
Section: Introductionmentioning
confidence: 99%