Chronic Pain Estimation Through Deep Facial Descriptors Analysis

Antoni Mauricio, Jonathan Peña, Erwin Dianderas, Leonidas Mauricio, Jose Díaz, Antonio Morán

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)


Worldwide, chronic pain has established as one of the foremost medical issues due to its 35% of comorbidity with depression and many other psychological problems. Traditionally, self-report (VAS scale) or physicist inspection (OPI scale) perform the pain assessment; nonetheless, both methods do not usually coincide [14]. Regarding self-assessment, several patients are not able to complete it objectively, like young children or patients with limited expression abilities. The lack of objectivity in the metrics draws the main problem of the clinical analysis of pain. In response, various efforts have tried concerning the inclusion of objective metrics, among which stand out the Prkachin and Solomon Pain Intensity (PSPI) metric defined by face appearance [5]. This work presents an in-depth learning approach to pain recognition considering deep facial representations and sequence analysis. Contrasting current state-of-the-art deep learning techniques, we correct rigid deformations caught since registration. A preprocessing stage is applied, which includes facial frontalization to untangle facial representations from non-affine transformations, perspective deformations, and outside noises passed since registration. After dealing with unbalanced data, we fine-tune a CNN from a pre-trained model to extract facial features, and then a multilayer RNN exploits temporal relation between video frames. As a result, we overcome state-of-the-art in terms of average accuracy at frames level (80.44%) and sequence level (84.54%) in the UNBC-McMaster Shoulder Pain Expression Archive Database.

Idioma originalInglés
Título de la publicación alojadaInformation Management and Big Data - 6th International Conference, SIMBig 2019, Proceedings
EditoresJuan Antonio Lossio-Ventura, Nelly Condori-Fernandez, Jorge Carlos Valverde-Rebaza
Número de páginas13
ISBN (versión impresa)9783030461393
EstadoPublicada - 2020
Publicado de forma externa
Evento6th International Conference on Information Management and Big Data, SIMBig 2019 - Lima, Perú
Duración: 21 ago. 201923 ago. 2019

Serie de la publicación

NombreCommunications in Computer and Information Science
Volumen1070 CCIS
ISSN (versión impresa)1865-0929
ISSN (versión digital)1865-0937


Conferencia6th International Conference on Information Management and Big Data, SIMBig 2019


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