Image-based measurement of changes to skin texture using piloerection for emotion estimation

被引:7
|
作者
Uchida, Mihiro [1 ]
Akaho, Rina [2 ]
Ogawa-Ochiai, Keiko [3 ]
Tsumura, Norimichi [4 ]
机构
[1] Chiba Univ, Grad Sch Sci & Engn, Inage Ku, 1-33 Yayoi Cho, Chiba, Chiba 2638522, Japan
[2] Chiba Univ, Grad Sch Adv Integrat Sci, Inage Ku, 1-33 Yayoi Cho, Chiba, Chiba 2638522, Japan
[3] Kanazawa Univ Hosp, Dept Japanese Tradit Kampo Med, 13-1 Takaramachi, Kanazawa, Ishikawa 9208641, Japan
[4] Chiba Univ, Grad Sch Engn, Inage Ku, 1-33 Yayoi Cho, Chiba, Chiba 2638522, Japan
基金
日本学术振兴会;
关键词
Emotion estimation; Goose bump; Image processing; Piloerection; Skin texture;
D O I
10.1007/s10015-018-0435-0
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
In this paper, we find effective feature values for skin texture as captured by a non-contact camera to monitor piloerection on the skin to estimate emotion. Piloerection is observed as goose bumps on the skin when a person is emotionally moved or scared. This phenomenon is caused by the contraction of the arrector pili muscles with the activation of the sympathetic nervous system. Piloerection changes skin texture, because of which we think it effective to examine skin texture to estimate the subject's emotions. Skin texture is important in the cosmetic industry to evaluate skin condition. Therefore, we thought that it will be effective to evaluate the condition of skin texture for emotion estimation. Evaluations were performed by extracting effective feature values from skin textures captured by using a high-resolution camera, where these feature values should be highly correlated with the degree of piloerection. The results showed that the feature value the standard deviation of short-line inclination angles in texture was satisfactorily correlated with the degree of piloerection.
引用
收藏
页码:12 / 18
页数:7
相关论文
共 50 条
  • [41] Skin Pores Detection for Image-Based Skin Analysis
    Zhang, Qian
    Whangbo, Taegkeun
    INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2008, 2008, 5326 : 233 - 240
  • [42] Door and window image-based measurement using a mobile device
    Ma, Guangyao
    Janakaraj, Manishankar
    Agam, Gady
    MOBILE DEVICES AND MULTIMEDIA: ENABLING TECHNOLOGIES, ALGORITHMS, AND APPLICATIONS 2015, 2015, 9411
  • [43] Image-based MRI gradient estimation
    Acquaviva, Roberto
    Mangione, Stefano
    Garbo, Giovanni
    MAGNETIC RESONANCE IMAGING, 2018, 49 : 138 - 144
  • [44] Seamless Image-Based Texture Atlases using Multi-band Blending
    Allene, Cedric
    Pons, Jean-Philippe
    Keriven, Renaud
    19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6, 2008, : 2539 - 2542
  • [45] Image-based classification of paper surface quality using wavelet texture analysis
    Reis, Marco S.
    Bauer, Armin
    COMPUTERS & CHEMICAL ENGINEERING, 2010, 34 (12) : 2014 - 2021
  • [46] Leaf Image-Based Plant Disease Identification Using Color and Texture Features
    Ahmad, Nisar
    Asif, Hafiz Muhammad Shahzad
    Saleem, Gulshan
    Younus, Muhammad Usman
    Anwar, Sadia
    Anjum, Muhammad Rizwan
    WIRELESS PERSONAL COMMUNICATIONS, 2021, 121 (02) : 1139 - 1168
  • [47] Leaf Image-Based Plant Disease Identification Using Color and Texture Features
    Nisar Ahmad
    Hafiz Muhammad Shahzad Asif
    Gulshan Saleem
    Muhammad Usman Younus
    Sadia Anwar
    Muhammad Rizwan Anjum
    Wireless Personal Communications, 2021, 121 : 1139 - 1168
  • [48] IMAGE-BASED SATELLITE ATTITUDE ESTIMATION
    Perrier, Regis
    Arnaud, Elise
    Sturm, Peter
    Ortner, Mathias
    2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2010, : 3394 - 3397
  • [49] Efficient image-based projective mapping using the master texture space encoding
    Guinnip, D
    Rice, D
    Jaynes, C
    Stevens, R
    WSCG 2003 SHORT PAPERS, PROCEEDINGS, 2003, : 49 - 56
  • [50] Image-based plant wilting estimation
    Changye Yang
    Sriram Baireddy
    Valérian Méline
    Enyu Cai
    Denise Caldwell
    Anjali S. Iyer-Pascuzzi
    Edward J. Delp
    Plant Methods, 19