TY - GEN
T1 - Indian Semi-Acted Facial Expression (iSAFE) Dataset for Human Emotions Recognition
AU - Singh, Shivendra
AU - Benedict, Shajulin
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2020.
PY - 2020
Y1 - 2020
N2 - Human emotion recognition is an imperative step to handle human computer interactions. It supports several machine learning based applications, including IoT cloud societal applications such as smart driving or smart living applications or medical applications. In fact, the dataset relating to human emotions remains as a crucial pre-requisite for designing efficient machine learning algorithms or applications. The traditionally available datasets are not specific to the Indian context, which lead to an arduous task for designing efficient region-specific applications. In this paper, we propose a new dataset that reveals the human emotions that are specific to India. The proposed dataset was developed at the IoT Cloud Research Laboratory of IIIT-Kottayam – the dataset contains 395 clips of 44 volunteers between 17 to 22 years of age; face expressions were captured when volunteers were asked to watch a few stimulant videos; the facial expressions were self annotated by the volunteers and they were cross annotated by annotators. In addition, the developed dataset was analyzed using ResNet34 neural network and the baseline of the dataset was provided for future research and developments in the human computer interaction domain.
AB - Human emotion recognition is an imperative step to handle human computer interactions. It supports several machine learning based applications, including IoT cloud societal applications such as smart driving or smart living applications or medical applications. In fact, the dataset relating to human emotions remains as a crucial pre-requisite for designing efficient machine learning algorithms or applications. The traditionally available datasets are not specific to the Indian context, which lead to an arduous task for designing efficient region-specific applications. In this paper, we propose a new dataset that reveals the human emotions that are specific to India. The proposed dataset was developed at the IoT Cloud Research Laboratory of IIIT-Kottayam – the dataset contains 395 clips of 44 volunteers between 17 to 22 years of age; face expressions were captured when volunteers were asked to watch a few stimulant videos; the facial expressions were self annotated by the volunteers and they were cross annotated by annotators. In addition, the developed dataset was analyzed using ResNet34 neural network and the baseline of the dataset was provided for future research and developments in the human computer interaction domain.
KW - Affective computing
KW - Facial expression recognition
KW - Human computer interaction
KW - Human emotions
UR - https://www.scopus.com/pages/publications/85084801175
U2 - 10.1007/978-981-15-4828-4_13
DO - 10.1007/978-981-15-4828-4_13
M3 - Conference contribution
AN - SCOPUS:85084801175
SN - 9789811548277
T3 - Communications in Computer and Information Science
SP - 150
EP - 162
BT - Advances in Signal Processing and Intelligent Recognition Systems - 5th International Symposium, SIRS 2019, Revised Selected Papers
A2 - Thampi, Sabu M.
A2 - Hegde, Rajesh M.
A2 - Krishnan, Sri
A2 - Mukhopadhyay, Jayanta
A2 - Chaudhary, Vipin
A2 - Marques, Oge
A2 - Piramuthu, Selwyn
A2 - Corchado, Juan M.
PB - Springer
T2 - 5th International Symposium on Signal Processing and Intelligent Recognition Systems, SIRS 2019
Y2 - 18 December 2019 through 21 December 2019
ER -