Estudio cualitativo de reconocimiento de emociones en tiempo real para atención al cliente utilizando deeplens face detection

Miguel Quiroz Martinez, Amparito Balseca Quirumbay, Maikel Leyva Vázquez

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


At present, a growing interest in the emotional state that employees present in customer service within organizations and companies is a fundamental point that helps guiding the behavior and thought processes in order to achieve good contact with those who request their services. This study, which has mainly a qualitative characteristic, was carried out with the staff working in the administrative area of the Educational Unit "Santa Maria Goretti", Ecuador. Within the framework of their job performance, this study aims to recognize the state of excitement of the employee. The captured image was processed giving rise to the recognition of emotions through facial features using a 4-layer convolutional network. The result of the sampling is subsequently presented, obtaining emotions in the range from which it presents greater percentage weighting to the lowest. Through the training process it was allowed to capture physical features in real time using the DeepLens face detection application, we worked with faces at different angles, this application emphasizes three main modules, these are namely, 1) face detection, 2) removal of features, and 3) classification of expressions. Before performing the task of emotion recognition, the facial recognition software detected the face and a series of key points as eyes, lips, eyebrows. and cheeks, resulting in the capture of employee's excitement, and the percentages of seven emotions, disgust, surprise, fear, angriness, happiness, sadness, and neutral.

Original languageEnglish
Pages (from-to)63-72
Number of pages10
JournalInvestigacion Operacional
Issue number1
StatePublished - 2021

Bibliographical note

Publisher Copyright:
© 2021 Universidad de La Habana. All rights reserved.


  • Artificial Intelligence
  • Convolutional Neural Network
  • Emotional Intelligence
  • Emotional Recognition
  • Facial Expression


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