Literature DB >> 34040673

Geometrical features of lips using the properties of parabola for recognizing facial expression.

V Suma Avani1, S G Shaila1, A Vadivel2.   

Abstract

Various real-time applications such as Human-Computer Interactions, Psychometric analysis, etc. use facial expressions as one of the important parameters. The researchers have used Action Units (AU) of the face as feature points and its deformation is compared with the reference points on the face to estimate the facial expressions. Among many parts of the face, features from the mouth contribute largely to all the well-known emotions. In this paper, the parabola theory is used to identify and mark various points on the lips. These points are considered as feature points to construct feature vectors. The Latus Rectum, Focal Point, Directrix, Vertex, etc. are also considered to identify the feature points of the lower lips and upper lips. The proposed approach is evaluated on benchmark datasets such as JAFFEE and Cohn-Kanade dataset and it is found that the performance is encouraging in understanding the facial expressions. The results are compared with contemporary methods and found that the proposed approach has given good classification accuracy in recognizing facial expressions. © Springer Nature B.V. 2020.

Entities:  

Keywords:  Emotions; Facial expressions; Lips; Mouth; Parabola

Year:  2020        PMID: 34040673      PMCID: PMC8131477          DOI: 10.1007/s11571-020-09638-x

Source DB:  PubMed          Journal:  Cogn Neurodyn        ISSN: 1871-4080            Impact factor:   3.473


  7 in total

1.  Effects of the duration of expressions on the recognition of microexpressions.

Authors:  Xun-bing Shen; Qi Wu; Xiao-lan Fu
Journal:  J Zhejiang Univ Sci B       Date:  2012-03       Impact factor: 3.066

2.  Object detection with discriminatively trained part-based models.

Authors:  Pedro F Felzenszwalb; Ross B Girshick; David McAllester; Deva Ramanan
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  2010-09       Impact factor: 6.226

3.  A Micro-GA Embedded PSO Feature Selection Approach to Intelligent Facial Emotion Recognition.

Authors:  Kamlesh Mistry; Li Zhang; Siew Chin Neoh; Chee Peng Lim; Ben Fielding
Journal:  IEEE Trans Cybern       Date:  2016-04-21       Impact factor: 11.448

4.  Face recognition by independent component analysis.

Authors:  M S Bartlett; J R Movellan; T J Sejnowski
Journal:  IEEE Trans Neural Netw       Date:  2002

5.  Hierarchical recognition scheme for human facial expression recognition systems.

Authors:  Muhammad Hameed Siddiqi; Sungyoung Lee; Young-Koo Lee; Adil Mehmood Khan; Phan Tran Ho Truc
Journal:  Sensors (Basel)       Date:  2013-12-05       Impact factor: 3.576

6.  Geometric feature-based facial expression recognition in image sequences using multi-class AdaBoost and support vector machines.

Authors:  Deepak Ghimire; Joonwhoan Lee
Journal:  Sensors (Basel)       Date:  2013-06-14       Impact factor: 3.576

7.  Facial expression recognition and histograms of oriented gradients: a comprehensive study.

Authors:  Pierluigi Carcagnì; Marco Del Coco; Marco Leo; Cosimo Distante
Journal:  Springerplus       Date:  2015-10-26
  7 in total
  1 in total

1.  Hierarchical scale convolutional neural network for facial expression recognition.

Authors:  Xinqi Fan; Mingjie Jiang; Ali Raza Shahid; Hong Yan
Journal:  Cogn Neurodyn       Date:  2022-01-05       Impact factor: 3.473

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.