Evaluation of Deep Convolutional Neural Network architectures for Emotion Recognition in the Wild

Talipu, Abudukaiyoumu; Generosi, Andrea; Mengoni, Maura; Giraldi, Luca

doi:10.1109/isce.2019.8900994

Cited by 20 publications

(16 citation statements)

References 5 publications

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“…Infrared cameras were positioned in the Sferisterio arena to detect the facial audience expression during the show. The soft-ware adopted in this context implements a combination of facial recognition and gaze tracking technologies based on artificial intelligence algorithms, as described in detail in Generosi et al (2020) and Talipu et al (2019). It enables the age and gender recognition of people shot by the cameras, monitoring their emotional state and corresponding level of interest and involvement.…”

Section: Audience Measurementmentioning

confidence: 99%

How to Create Engaging Experiences From Human Emotions

Mengoni

Ceccacci

Giraldi

et al. 2021

Diid

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There are several situations where the success of a product or a service depends on people’s emotional reactions while interacting with it. Feelings depends on the personal reaction to the values it offers. Therefore, it is not possible to presume that all persons will share the same reactions. An important challenge that designers have to face is to measure such feelings at every digital and physical touchpoint encountered by the customer and in which an experience takes place. In this context, the present research proposes an emotional-sensitive system able to measure the customer’s emotion and behavior and responsively adapt the experience to generate an individualized human-system interaction. It enables a continuous flow between analysis and reaction at every touchpoint of the customer experience. The paper illustrates the implementing technological framework, developed by EMOJ, an Italian startup leader in Artificial Intelligence (AI), and several applicative use cases.

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Section: Audience Measurementmentioning

confidence: 99%

How to Create Engaging Experiences From Human Emotions

Mengoni

Ceccacci

Giraldi

et al. 2021

Diid

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“…The Emotion Recognition module is based on a Convolutional Neural Network (CNN) trained using a merged dataset with both "in the wild'' and "in lab" properties, and the Python version of Tensorflow and Keras frameworks (Talipu et al, 2019). In particular, this CNN has been trained with the public dataset CK+ (Lucey et al, 2010) and FER+ (Barsoum et al, 2016) built in laboratory, and the "in the wild '' dataset provided by Affectnet.…”

Section: Emotion Recognition Modulementioning

confidence: 99%

Designing in-car emotion-aware automation

Ceccacci¹

2021

European Transport/Trasporti Europei

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Driver behaviour recognition is of paramount importance for in-car automation assistance. It is widely recognized that not only attentional states, but also emotional ones have an impact on the safety of the driving behaviour. This research work proposes an emotion-aware in-car architecture where it is possible to adapt driver’s emotions to the vehicle dynamics, investigating the correlations between negative emotional states and driving performances, and suggesting a system to regulate the driver’s engagement through a unique user experience (e.g. using music, LED lighting) in the car cabin. The relationship between altered emotional states induced through auditory stimuli and vehicle dynamics is investigated in a driving simulator. The results confirm the need for both types of information to improve the robustness of the driver state recognition function and open up the possibility that auditory stimuli can modify driving performance somehow.

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“…Feed Forward Neural Network is an artificial neural network (ANN). It used for classification of class labels [14]. It is developed from the biological phenomenon that takes place in human body.…”

Section: Proposed Workmentioning

confidence: 99%

Emotion Recognition using Feed Forward Neural Network & Naïve Bayes

Shahane*¹,

.K²,

Siddeeq³

2019

IJITEE

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In this paper we analyze and predict the emotion of a user by recognizing his/her face. Face recognition is a software application which is used to identify a particular person; it will be mostly useful in security applications to secure our data. Now a day we are using face unlock in mobiles to unlock our phones. We need to know the emotions of a person in some situations. Though we can recognize his emotion through his tone of voice, it would be more helpful if get to know his emotions. This can be much helpful in finding out a criminal by finding out his emotion whether he is feeling nervous or not which expresses his/her fear by this. In order to analyze his/her emotion firstly we need to recognize his/her face, so we need to use face recognition method and then implement emotion analysis. Here we use different algorithms to implement emotion analysis such as CNN. We will have the dataset with pixels and emotion this will be the training data. Then we will be initially taking the picture and then convert them to pixels these will be acting as the testing data. We then use an algorithm to predict these pixels emotion which is nothing but predicting the emotion of the picture taken

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Evaluation of Deep Convolutional Neural Network architectures for Emotion Recognition in the Wild

Cited by 20 publications

References 5 publications

How to Create Engaging Experiences From Human Emotions

How to Create Engaging Experiences From Human Emotions

Designing in-car emotion-aware automation

Emotion Recognition using Feed Forward Neural Network & Naïve Bayes

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