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Inferring intrinsic correlation between clothing style and wearers’ personality

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Abstract

Clothing, as a language of signs, transmit more information of wearers’ inner-self. Specially, portraits and Selfies take a high percent on social networking. Is there any relationship between wearers’ personality and a range of relevant clothing features? In this work, we intend to explore the inherent relationship between wearers’ personality type and expressive wearing. First, a sufficiently large dataset was built based on the theory of personality type. More than 300 celebrities who were classified according to the personality theory have been collected and the images of their wearing were downloaded from Google Image. To understand the intrinsic characteristics of different style of clothing, a suite of image analysis algorithms, including face detection, approximately body detection, clothing area detection using GrabCut algorithm and skin detection, clothing features extraction were developed in this research. Statistical analysis with Binary Logistic verified the significance level of extracted features correlated with personality types. Experimental results demonstrated that the proposed scheme can achieve higher precision accuracy through an SVM (Support Vector Machine) scheme than simple binary classification.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No.61672475, No.61402428, and No.61602430); Qingdao Science and Technology Development Plan (No. 16-5-1-13-jch).

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Authors and Affiliations

  1. College of Information Science and Engineering, Ocean University of China, Qingdao, China

    Zhiqiang Wei, Yan Yan, Lei Huang & Jie Nie

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  1. Zhiqiang Wei
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  2. Yan Yan
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  3. Lei Huang
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  4. Jie Nie
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Correspondence to Yan Yan.

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Wei, Z., Yan, Y., Huang, L. et al. Inferring intrinsic correlation between clothing style and wearers’ personality. Multimed Tools Appl 76, 20273–20285 (2017). https://doi.org/10.1007/s11042-017-4778-7

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  • DOI: https://doi.org/10.1007/s11042-017-4778-7

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