Sequential voting by veto: Making the Mueller-Moulin algorithm more versatile

S. Dan Felsenthal; Moshé Machover

doi:10.1007/bf00133642

Sequential voting by veto: Making the Mueller-Moulin algorithm more versatile

Theory and Decision ◽

10.1007/bf00133642 ◽

1992 ◽

Vol 33 (3) ◽

pp. 223-240 ◽

Cited By ~ 9

Author(s):

S. Dan Felsenthal ◽

Moshé Machover

Keyword(s):

Voting By Veto ◽

Sequential Voting

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A lemma in open sequential voting by veto

Mathematical Social Sciences ◽

10.1016/j.mathsocsci.2017.07.003 ◽

2017 ◽

Vol 90 ◽

pp. 141-144 ◽

Cited By ~ 2

Author(s):

Natalia M. Novikova ◽

Irina I. Pospelova

Keyword(s):

Voting By Veto ◽

Sequential Voting

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Sequential Voting by Veto

Systems, Procedures and Voting Rules in Context - Advances in Group Decision and Negotiation ◽

10.1007/978-3-030-30955-8_6 ◽

2019 ◽

pp. 51-56

Author(s):

Adiel Teixeira de Almeida ◽

Danielle Costa Morais ◽

Hannu Nurmi

Keyword(s):

Voting By Veto ◽

Sequential Voting

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Voting by veto and the role of the compromise function

Mathematical Social Sciences ◽

10.1016/s0165-4896(96)00827-x ◽

1997 ◽

Vol 33 (2) ◽

pp. 101-113

Author(s):

Daniel Cardona-Coll

Keyword(s):

Voting By Veto

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Emotion Categorization from Video-Frame Images Using a Novel Sequential Voting Technique

10.26686/wgtn.14411021.v1 ◽

2021 ◽

Author(s):

Harisu Abdullahi Shehu ◽

William Browne ◽

Hedwig Eisenbarth

Keyword(s):

Machine Learning ◽

Facial Expression ◽

Facial Expressions ◽

Classification Accuracy ◽

Video Frame ◽

Video Databases ◽

Machine Learning Classifiers ◽

Learning Classifiers ◽

Good Classification ◽

Sequential Voting

Emotion categorization can be the process of identifying different emotions in humans based on their facial expressions. It requires time and sometimes it is hard for human classifiers to agree with each other about an emotion category of a facial expression. However, machine learning classifiers have done well in classifying different emotions and have widely been used in recent years to facilitate the task of emotion categorization. Much research on emotion video databases uses a few frames from when emotion is expressed at peak to classify emotion, which might not give a good classification accuracy when predicting frames where the emotion is less intense. In this paper, using the CK+ emotion dataset as an example, we use more frames to analyze emotion from mid and peak frame images and compared our results to a method using fewer peak frames. Furthermore, we propose an approach based on sequential voting and apply it to more frames of the CK+ database. Our approach resulted in up to 85.9% accuracy for the mid frames and overall accuracy of 96.5% for the CK+ database compared with the accuracy of 73.4% and 93.8% from existing techniques.

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