October 17, 2018 Working paper Open Access

Batziou Elissavet; Michail Emmanouil; Avgerinakis Konstantinos; Vrochidis Stefanos; Patras Ioannis; Kompatsiaris Ioannis

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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.3491644", 
  "author": [
    {
      "family": "Batziou Elissavet"
    }, 
    {
      "family": "Michail Emmanouil"
    }, 
    {
      "family": "Avgerinakis Konstantinos"
    }, 
    {
      "family": "Vrochidis Stefanos"
    }, 
    {
      "family": "Patras Ioannis"
    }, 
    {
      "family": "Kompatsiaris  Ioannis"
    }
  ], 
  "issued": {
    "date-parts": [
      [
        2018, 
        10, 
        17
      ]
    ]
  }, 
  "abstract": "<p>This work reports the methodology that CERTH-ITI team developed so as to recognize the emotional impact that movies have to its viewers in terms of valence/arousal and fear. More Specifically,deep convolutional neural networks and several machine learning techniques are utilized to extract visual features and classify them based on the predicted model, while audio features are also taken into account in the fear scenario, leading to highly accurate recognition rates.</p>", 
  "title": "Visual and audio analysis of movies video for emotion detection@ Emotional Impact of Movies task MediaEval 2018", 
  "type": "article", 
  "id": "3491644"
}