Rasabodha: Understanding Indian classical dance by recognizing emotions using deep learning. (July 2018)
- Record Type:
- Journal Article
- Title:
- Rasabodha: Understanding Indian classical dance by recognizing emotions using deep learning. (July 2018)
- Main Title:
- Rasabodha: Understanding Indian classical dance by recognizing emotions using deep learning
- Authors:
- Mohanty, Aparna
Sahay, Rajiv R. - Abstract:
- Highlights: A deep learning based approach using CNN to recognize emotions in Indian classical dance. Three datasets are proposed for Navarasas enacted in Indian classical dance. The proposed algorithm was demonstrated to be useful in understanding short video clips corresponding to Shlokas and in training of novice dancers. Abstract: Understanding human behavior using computer vision techniques for recognizing body posture, gait, hand gesture, and facial expressions has recently witnessed significant research activity. Emotions/affect have a direct correlation with the mental state, as well as intention of a person, based on which his/her present and future states can be understood and predicted. As a case study in this work, we demonstrate the utility of deep learning in understanding videos of Indian classical dance (ICD) forms. ICD comprises hand gestures, body poses and facial expressions enacted by the performer along with the accompanying music and songs/ Shlokas . In this work we attempt to decipher the meaning of Navarasas associated with Indian classical dance (ICD). Recognizing these emotions from images/videos of ICD is a challenge due to factors such as ambiguity in the enactment, costume, make-up, clutter, etc. Here, we propose a dataset of various emotions ( Navarasas ) enacted in ICD comprising RGB images along with associated depth information collected using the Microsoft Kinect sensor. We propose a deep learning framework using convolutional neuralHighlights: A deep learning based approach using CNN to recognize emotions in Indian classical dance. Three datasets are proposed for Navarasas enacted in Indian classical dance. The proposed algorithm was demonstrated to be useful in understanding short video clips corresponding to Shlokas and in training of novice dancers. Abstract: Understanding human behavior using computer vision techniques for recognizing body posture, gait, hand gesture, and facial expressions has recently witnessed significant research activity. Emotions/affect have a direct correlation with the mental state, as well as intention of a person, based on which his/her present and future states can be understood and predicted. As a case study in this work, we demonstrate the utility of deep learning in understanding videos of Indian classical dance (ICD) forms. ICD comprises hand gestures, body poses and facial expressions enacted by the performer along with the accompanying music and songs/ Shlokas . In this work we attempt to decipher the meaning of Navarasas associated with Indian classical dance (ICD). Recognizing these emotions from images/videos of ICD is a challenge due to factors such as ambiguity in the enactment, costume, make-up, clutter, etc. Here, we propose a dataset of various emotions ( Navarasas ) enacted in ICD comprising RGB images along with associated depth information collected using the Microsoft Kinect sensor. We propose a deep learning framework using convolutional neural networks to understand the semantic meaning associated with videos of ICD by recognizing Navarasas enacted by the performer. … (more)
- Is Part Of:
- Pattern recognition. Volume 79(2018:Jul.)
- Journal:
- Pattern recognition
- Issue:
- Volume 79(2018:Jul.)
- Issue Display:
- Volume 79 (2018)
- Year:
- 2018
- Volume:
- 79
- Issue Sort Value:
- 2018-0079-0000-0000
- Page Start:
- 97
- Page End:
- 113
- Publication Date:
- 2018-07
- Subjects:
- Deep learning -- Convolutional neural network -- Facial expression recognition -- Emotion recognition
Pattern perception -- Periodicals
Perception des structures -- Périodiques
Patroonherkenning
006.4 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00313203 ↗
http://www.sciencedirect.com/ ↗ - DOI:
- 10.1016/j.patcog.2018.01.035 ↗
- Languages:
- English
- ISSNs:
- 0031-3203
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 20792.xml