In the near infrared image containing complex background, eye detection is a great challenge for a low signal to noise ratio and lacking of shape and texture information. Aiming at the problem of accurate eye localization and segmentation, simplified pulse coupled neural networks (SPCNN) combined with morphology method is put forward. The contributions of this work can be divided into two parts. The first contribution is that the local region of the eyes is extracted efficiently via morphology opening top-hat operator, as the region of interest for ensuring the follow-up processing without interference of the background. The second contribution is that a SPCNN model is proposed to carefully partition pixels into a corresponding cluster in iterative manner for ensuring high segmentation performance. Experiments are carried out on the near infrared images obtained by the designed acquisition system using the proposed method as well as Otsu and k-means for comparison. Experimental results show that our method achieves desired segmentation performance and has a lower misclassification error.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.