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.