TY  - JOUR
T1  - Facial Affect Recognition and Impact of Affect Arousal on Health Data
AU - Dixit, Bharati AU - Gaikwad, Arun 
JO  - Journal of Engineering and Applied Sciences
VL  - 14
IS  - 16
SP  - 5733
EP  - 5742
PY  - 2019
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2019.5733.5742
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2019.5733.5742
KW  - Affect recognition
KW  -affect health data
KW  -emotion profile
KW  -blood pressure deviation
KW  -image database
KW  -respect
AB  - Automatic recognition of emotions is a challenging task and can be performed using single modal
or multimodal inputs. It can enhance the effectiveness of human machine interaction systems and need of the
time as it has applications in various domains. The study discusses the experiments carried out to capture and
analyze the data which is related to human health. Images of the subjects under study are captured along with
human health data and the degree of presence/absence of all seven universally accepted emotions is derived
from images and depicted in the form of emotion profile. Facial affect recognition is performed on MIST
database a locally created context specific database of images with health data like pulse rate, systolic and
diastolic blood pressure. Emotions are categorized as neutral, positive and negative for MIST database.
Accuracy obtained as 91.38%. Affect health data is analyzed for deviation in positive and negative emotion
category with reference to neutral category. The inference derived from this affect health data analysis is that
the observed deviation for negative emotions with respect to neutral emotion category falls into high deviation
ranges for more No. of subjects as compare to deviation for positive emotions with respect to neutral emotion
category.
ER  -