Optical Review

, Volume 24, Issue 2, pp 117–120 | Cite as

Object recognition through a multi-mode fiber

  • Ryosuke Takagi
  • Ryoichi Horisaki
  • Jun Tanida
Regular Paper


We present a method of recognizing an object through a multi-mode fiber. A number of speckle patterns transmitted through a multi-mode fiber are provided to a classifier based on machine learning. We experimentally demonstrated binary classification of face and non-face targets based on the method. The measurement process of the experimental setup was random and nonlinear because a multi-mode fiber is a typical strongly scattering medium and any reference light was not used in our setup. Comparisons between three supervised learning methods, support vector machine, adaptive boosting, and neural network, are also provided. All of those learning methods achieved high accuracy rates at about 90% for the classification. The approach presented here can realize a compact and smart optical sensor. It is practically useful for medical applications, such as endoscopy. Also our study indicated a promising utilization of artificial intelligence, which has rapidly progressed, for reducing optical and computational costs in optical sensing systems.


Object recognition and classification Machine learning Multi-mode fiber Support vector machine Adaptive boosting Neural network 



This work was supported by JSPS KAKENHI Grant Number 15K13381.


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Copyright information

© The Optical Society of Japan 2017

Authors and Affiliations

  1. 1.Department of Information and Physical Sciences, Graduate School of Information Science and TechnologyOsaka UniversityOsakaJapan

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