Fast Iris localization using Haar-like features and AdaBoost algorithm


Traditional iris recognition methods, which are still preferred against artificial intelligence (AI) approaches in practical applications, are often required to capture high-grade iris samples by an iris scanner for accurate subsequent processing. To reduce the system cost for mass deployment of iris recognition, pricey scan devices can be replaced by the average quality cameras combined with additional processing algorithm. In this paper, we propose a Haar-like-feature-based iris localization method to quickly detect the location of human iris in the images captured by low-cost cameras for the ease of post-processing stages. The AdaBoost algorithm was chosen as a learning method for training a cascade classifier using Haar-like features, which was then utilized to detect the iris position. The experimental results have shown acceptable accuracy and processing speed for this novel cascade classifier. This achievement stimulates us to implement this novel capturing device in our iris recognition.

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The authors are very grateful to the anonymous reviewers for their constructive comments which have improved the quality of this paper. Also, this work was supported by the Ministry of Science and Technology, Taiwan, under grant MOST 107-2221-E-845-001-MY3 and MOST 107-2221-E-845- 002-MY3.

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Correspondence to Victor R. L. Shen.

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Lin, Y., Hsieh, T., Huang, J. et al. Fast Iris localization using Haar-like features and AdaBoost algorithm. Multimed Tools Appl (2020).

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  • Object detection
  • Iris localization
  • Haar-like features
  • AdaBoost algorithm
  • Cascade classifier