“AffectiveWear: Toward Recognizing Facial Expression” by Masai, Sugiura, Ogata, Suzuki, Nakamura, et al. …

  • ©Katsutoshi Masai, Yuta Sugiura, Masa Ogata, Katsuhiro Suzuki, Fumihiko Nakamura, Sho Shimamura, Kai Kunze, Masahiko Inami, and Maki Sugimoto

  • ©Katsutoshi Masai, Yuta Sugiura, Masa Ogata, Katsuhiro Suzuki, Fumihiko Nakamura, Sho Shimamura, Kai Kunze, Masahiko Inami, and Maki Sugimoto

  • ©Katsutoshi Masai, Yuta Sugiura, Masa Ogata, Katsuhiro Suzuki, Fumihiko Nakamura, Sho Shimamura, Kai Kunze, Masahiko Inami, and Maki Sugimoto

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Entry Number: 16

Title:

    AffectiveWear: Toward Recognizing Facial Expression

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Abstract:


    Facial expression is a powerful way for us to exchange information nonverbally. They can give us insights into how people feel and think. There are a number of works related to facial expression detection in computer vision. However, most works focus on camera-based systems installed in the environment. With this method, it is difficult to track user’s face if user moves constantly. Moreover, user’s facial expression can be recognized at only a limited place.
    We present the eyewear that can detect facial expression anytime, anywhere (Figure 1 a). This eyewear can categorize 7 facial expressions by measuring the distance between an eyewear frame and a skin surface of a person’s face with 8 photo reflective sensors. Recognizable states are as follows: neutral, smile, laugh, disgust, angry, sad and surprise. With our method, an individual difference can be ignored with user-dependent training. Several works show the wearable systems that can recognize facial expression. Yet, these works focus on detecting only one specific facial expression. Our contribution is detecting 7 facial expression states in daily life. With our device, user can better understand their mind, and computing systems can tap into the rich set of information provided by nonverbal communication.


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©Katsutoshi Masai, Yuta Sugiura, Masa Ogata, Katsuhiro Suzuki, Fumihiko Nakamura, Sho Shimamura, Kai Kunze, Masahiko Inami, and Maki Sugimoto ©Katsutoshi Masai, Yuta Sugiura, Masa Ogata, Katsuhiro Suzuki, Fumihiko Nakamura, Sho Shimamura, Kai Kunze, Masahiko Inami, and Maki Sugimoto

Acknowledgements:


    This research was supported by CREST, JST.


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