DocumentCode :
3185532
Title :
Predicting dominance judgements automatically: A machine learning approach
Author :
Rojas Q, Mario ; Masip, David ; Vitrià, Jordi
Author_Institution :
Comput. Vision Center, Univ. Autonoma de Barcelona, Barcelona, Spain
fYear :
2011
fDate :
21-25 March 2011
Firstpage :
939
Lastpage :
944
Abstract :
The amount of multimodal devices that surround us is growing everyday. In this context, human interaction and communication have become a focus of attention and a hot topic of research. A crucial element in human relations is the evaluation of individuals with respect to facial traits, what is called a first impression. Studies based on appearance have suggested that personality can be expressed by appearance and the observer may use such information to form judgments. In the context of rapid facial evaluation, certain personality traits seem to have a more pronounced effect on the relations and perceptions inside groups. The perception of dominance has been shown to be an active part of social roles at different stages of life, and even play a part in mate selection. The aim of this paper is to study to what extent this information is learnable from the point of view of computer science. Specifically we intend to determine if judgments of dominance can be learned by machine learning techniques. We implement two different descriptors in order to assess this. The first is the histogram of oriented gradients (HOG), and the second is a probabilistic appearance descriptor based on the frequencies of grouped binary tests. State of the art classification rules validate the performance of both descriptors, with respect to the prediction task. Experimental results show that machine learning techniques can predict judgments of dominance rather accurately (accuracies up to 90%) and that the HOG descriptor may characterize appropriately the information necessary for such task.
Keywords :
face recognition; learning (artificial intelligence); probability; HOG; dominance judgement; facial trait; first impression; histogram of oriented gradient; human communication; human interaction; human relation; machine learning; multimodal device; personality trait; probabilistic appearance descriptor; rapid facial evaluation; Accuracy; Context; Face; Histograms; Humans; Machine learning; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Automatic Face & Gesture Recognition and Workshops (FG 2011), 2011 IEEE International Conference on
Conference_Location :
Santa Barbara, CA
Print_ISBN :
978-1-4244-9140-7
Type :
conf
DOI :
10.1109/FG.2011.5771377
Filename :
5771377
Link To Document :
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