• Corpus ID: 212462113

Algorithm for Analysis of Emotion Using Body Language

  title={Algorithm for Analysis of Emotion Using Body Language},
  author={Saurabh Singh and Ravi Kr. Bhall and Vishal Sharma},
399 www.erpublication.org  Abstract— This paper proposes a system which will detect emotion and mental state of a person by detecting the pose of a person, detection of emotion will be based on body parts not on facial expressions. Work done on emotion detection is basically done on facial expression, according to the psychological study it is found that every body part shows an expression. This whole detection is based on gestures of human body, we already have frameworks for facial… 

Figures and Tables from this paper

EDBL - algorithm for detection and analysis of emotion using body language
The experimental result shows that by using EDBL algorithm the authors can infer emotions and state of mind from human pose, in terms of body gesture including shoulder and hand, even if they exclude the facial expressions.
An adaptive e-learning environment centred on learner's emotional behaviour
  • A. Kanimozhi, V. C. Raj
  • Computer Science
    2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET)
  • 2017
To reveal the learner's emotional behavior, Facial feature emotion extraction, body gesture, movement and EEG — Bio signal approach for emotion prediction are taken and the result shows that bio-signal accurately predicting the learners emotion.


The primary purpose of this review is to provide positive view and prepare a platform for emotion detection using bodily emotions by looking at Laban Movement Analysis as a prominent and a promising method for movement representation in this field.
Emotion recognition in human-computer interaction
Basic issues in signal processing and analysis techniques for consolidating psychological and linguistic analyses of emotion are examined, motivated by the PKYSTA project, which aims to develop a hybrid system capable of using information from faces and voices to recognize people's emotions.
Mixing Body-Part Sequences for Human Pose Estimation
This paper introduces a new dataset "Poses in the Wild", which is more challenging than the existing ones, with sequences containing background clutter, occlusions, and severe camera motion and presents a new approximate scheme with two steps dedicated to pose estimation.
Learning Gender with Support Faces
Nonlinear support vector machines are investigated for appearance-based gender classification with low-resolution "thumbnail" faces processed from the FERET (FacE REcognition Technology) face database, demonstrating robustness and stability with respect to scale and the degree of facial detail.
Estimating Human Pose with Flowing Puppets
An approach for tracking articulated motions that "links" articulated shape models of people in adjacent frames through the dense optical flow that provides a way of integrating image evidence across frames to improve pose inference.
The Expression of the Emotions in Man and Animals
The Expression of the Emotions in Man and Animals Introduction to the First Edition and Discussion Index, by Phillip Prodger and Paul Ekman.
The Expression of the Emotions in Man and Animals
The Expression of the Emotions in Man and Animals is a book by Charles Darwin, published in 1872, concerning genetically determined aspects of behaviour. It was published thirteen years after On the
Learning hierarchical poselets for human parsing
A structured model to organize poselets in a hierarchical way and learn the model parameters in a max-margin framework and demonstrates the superior performance of the proposed approach on two datasets with aggressive pose variations.
Cascaded Models for Articulated Pose Estimation
This work proposes to learn a sequence of structured models at different pose resolutions, where coarse models filter the pose space for the next level via their max-marginals, and trains the cascade to prune as much as possible while preserving true poses for the final level pictorial structure model.
Pictorial Structures for Object Recognition
A computationally efficient framework for part-based modeling and recognition of objects, motivated by the pictorial structure models introduced by Fischler and Elschlager, that allows for qualitative descriptions of visual appearance and is suitable for generic recognition problems.