The FaceChannel: A Fast and Furious Deep Neural Network for Facial Expression Recognition

Barros, Pablo; Churamani, Nikhil; Sciutti, Alessandra

doi:10.1007/s42979-020-00325-6

Cited by 38 publications

(29 citation statements)

References 44 publications

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“…Analysing the different data sets in Figure 3, we believe that certain data enhancements will strengthen the image information and improve the recognition accuracy. Because the original pixels of the data are too low, we directly extract the feature map of its local binary mode, which will enhance the texture information while losing more information [12]. Therefore, the speed is the fastest when the accuracy is the lowest.…”

Section: Results Analysismentioning

confidence: 99%

[Retracted] Facial Expression Recognition Based on Convolutional Neural Network Fusion SIFT Features of Mobile Virtual Reality

Yao

Qiu

2021

Wireless Communications and Mobile Computing

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Facial expression recognition computer technology can obtain the emotional information of the person through the expression of the person to judge the state and intention of the person. The article proposes a hybrid model that combines a convolutional neural network (CNN) and dense SIFT features. This model is used for facial expression recognition. First, the article builds a CNN model and learns the local features of the eyes, eyebrows, and mouth. Then, the article features are sent to the support vector machine (SVM) multiclassifier to obtain the posterior probabilities of various features. Finally, the output result of the model is decided and fused to obtain the final recognition result. The experimental results show that the improved convolutional neural network structure ER2013 and CK+ data sets’ facial expression recognition rate increases by 0.06% and 2.25%, respectively.

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Section: Results Analysismentioning

confidence: 99%

[Retracted] Facial Expression Recognition Based on Convolutional Neural Network Fusion SIFT Features of Mobile Virtual Reality

Yao

Qiu

2021

Wireless Communications and Mobile Computing

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“…The FaceChannel was recently formalized as a lightweighted convolutional neural network that is able to adapt towards different interaction scenarios. It showed to have a good adaptation towards novel scenarios [3,5], including the challenging task of recognizing affect during a Human-Robot Interaction setting [7].…”

Section: The Masked Face Channelmentioning

confidence: 99%

“…Recently, we proposed the FaceChannel [3], as a small and easy to adapt neural network, and evaluated it on different scenarios, including different fine-tuning methods. Our experiments showed that the network could be easily adapted due to its small number of parameters while maintaining good performance on most of the facial expression datasets.…”

Section: Introductionmentioning

confidence: 99%

I Only Have Eyes for You: The Impact of Masks On Convolutional-Based Facial Expression Recognition

Barros¹,

Sciutti²

2021

Preprint

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The current COVID-19 pandemic has shown us that we are still facing unpredictable challenges in our society. The necessary constrain on social interactions affected heavily how we envision and prepare the future of social robots and artificial agents in general. Adapting current affective perception models towards constrained perception based on the hard separation between facial perception and affective understanding would help us to provide robust systems. In this paper, we perform an in-depth analysis of how recognizing affect from persons with masks differs from general facial expression perception. We evaluate how the recently proposed FaceChannel adapts towards recognizing facial expressions from persons with masks. In Our analysis, we evaluate different training and fine-tuning schemes to understand better the impact of masked facial expressions. We also perform specific feature-level visualization to demonstrate how the inherent capabilities of the FaceChannel to learn and combine facial features change when in a constrained social interaction scenario.

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“…Affective Data To analyze subjects' facial expressions we used the FaceChannel neural network [2]. The FaceChannel is a lightweight convolutional neural network that allows for fast training and fine-tuning of facial expressions.…”

Section: The Taskmentioning

confidence: 99%

Sensing the Partner: Toward Effective Robot Tutoring in Motor Skill Learning

et al. 2020

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Effective tutoring during motor learning requires to provide the appropriate physical assistance to the learners, but at the same time to assess and adapt to their state, to avoid frustration. With the aim of endowing robot tutors with these abilities, we designed an experiment in which participants had to acquire a new motor ability -balancing an unstable inverted pendulum -with the support of a robot providing fixed physical assistance. We analyzed participants' behavior and explicit evaluations to (i) identify the motor strategy associated with best performances in the task; (ii) assess whether natural facial expressions automatically extracted from cameras during task execution can inform about the participant's state. The results indicate that the variation and the mean of the wrist velocity are the most relevant in the effective balancing strategy, suggesting that a robot tutor could reorient the attention of the pupil on this parameter to facilitate the learning process. Moreover, facial expressions vary significantly during the task, especially in the dimension of Valence, which decreases with training. Interestingly, only when the robot had an anthropomorphic presence, Valence correlated with the degree of frustration experienced in the task. These findings highlight that both physical behavior and affective signals could be integrated by an autonomous robot to generate adaptive and individualized assistance, mindful both of the learning process and the partner's affective state.

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The FaceChannel: A Fast and Furious Deep Neural Network for Facial Expression Recognition

Cited by 38 publications

References 44 publications

[Retracted] Facial Expression Recognition Based on Convolutional Neural Network Fusion SIFT Features of Mobile Virtual Reality

[Retracted] Facial Expression Recognition Based on Convolutional Neural Network Fusion SIFT Features of Mobile Virtual Reality

I Only Have Eyes for You: The Impact of Masks On Convolutional-Based Facial Expression Recognition

Sensing the Partner: Toward Effective Robot Tutoring in Motor Skill Learning

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