HoloNet: towards robust emotion recognition in the wild

Anbang Yao · Dongqi Cai · Ping Hu · Shandong Wang · Yurong Chen

115 citations0/0 files storedACM International Conference on Multimodal Interaction (ICMI)

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Emotion and Mood Recognition Face recognition and analysis Face and Expression Recognition

Abstract

In this paper, we present HoloNet, a well-designed Convolutional Neural Network (CNN) architecture regarding our submissions to the video based sub-challenge of the Emotion Recognition in the Wild (EmotiW) 2016 challenge. In contrast to previous related methods that usually adopt relatively simple and shallow neural network architectures to address emotion recognition task, our HoloNet has three critical considerations in network design. (1) To reduce redundant filters and enhance the non-saturated non-linearity in the lower convolutional layers, we use a modified Concatenated Rectified Linear Unit (CReLU) instead of ReLU. (2) To enjoy the accuracy gain from considerably increased network depth and maintain efficiency, we combine residual structure and CReLU to construct the middle layers. (3) To broaden network width and introduce multi-scale feature extraction property, the topper layers are designed as a variant of inception-residual structure. The main benefit of grouping these modules into the HoloNet is that both negative and positive phase information implicitly contained in the input data can flow over it in multiple paths, thus deep multi-scale features explicitly capturing emotion variation can be well extracted from multi-path sibling layers, and then can be further concatenated for robust recognition. We obtain competitive results in this year’s video based emotion recognition sub-challenge using an ensemble of two HoloNet models trained with given data only. Specifically, we obtain a mean recognition rate of 57.84%, outperforming the baseline accuracy with an absolute margin of 17.37%, and yielding 4.04% absolute accuracy gain compared to the result of last year’s winner team. Meanwhile, our method runs with a speed of several thousands of frames per second on a GPU, thus it is well applicable to real-time scenarios.

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References

2008A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions 2013Combining modality specific deep neural networks for emotion recognition in video 2011Emotion recognition using a hierarchical binary decision tree approach 2015Video and Image based Emotion Recognition Challenges in the Wild 2015A mixed bag of emotions: Model, predict, and transfer emotion distributions 2014Emotion Recognition In The Wild Challenge 2014 2014Combining Multiple Kernel Methods on Riemannian Manifold for Emotion Recognition in the Wild 2013Emotion recognition in the wild challenge 2013

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2021Landmark guidance independent spatio-channel attention and complementary context information based facial expression recognition 2021A Survey of AI-Based Facial Emotion Recognition: Features, ML & DL Techniques, Age-Wise Datasets and Future Directions 2018A visual attention based ROI detection method for facial expression recognition 2018Multi-cue fusion for emotion recognition in the wild 2017Audio-visual emotion recognition using deep transfer learning and multiple temporal models

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