Stereoscopic Media Art That Changes Based on Gender Classification Using a Depth Sensor
Author(s) -
YoungEun Kim,
Sang-Hun Nam,
Jin-Wan Park
Publication year - 2014
Publication title -
international journal of distributed sensor networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.324
H-Index - 53
eISSN - 1550-1477
pISSN - 1550-1329
DOI - 10.1155/2014/430907
Subject(s) - computer science , stereoscopy , support vector machine , set (abstract data type) , preference , exhibition , human–computer interaction , interface (matter) , artificial intelligence , multimedia , visual arts , mathematics , statistics , art , bubble , maximum bubble pressure method , parallel computing , programming language
Physical and psychological characteristics of people vary depending on gender and are used in various fields including the arts such as media art. An interface that provides different interaction results based on the gender of the users enhances participant satisfaction. For gender classification in a dark environment such as an exhibition hall, a depth sensor that discerned between the human head and the body and a support vector machine (SVM) that classified internal factors were used. In terms of the stereoscopic media art, factors that influenced the audience were set to be color, depth, and velocity in a certain direction, and a survey was conducted to examine the preferences of men and women. After gender classification, the preference factors of men and women were applied to produce interactive media art that showed different results depending on the interaction. The possibility of the interface based on gender classification was identified through the survey on preferences between a conventional interactive system and an interactive system based on gender classification.
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