Near-Infrared Image-Based Periocular Biometric Method Using Convolutional Neural Network

The biometric technique of iris recognition is considerably limited by the cost of optical devices and user inconvenience. Periocular-based methods are an alternative means of biometric authentication because they do not require expensive equipment. Moreover, the resulting data are suitable for biometrics because they include features such as eyelashes, eyebrows, and eyelids. However, conventional periocular-based biometric authentication methods use limited sets of features that are dependent on the selected feature extraction method, resulting in relatively poor performance. Therefore, we propose a deep-learning-based method that actively utilizes the various features contained in periocular images. The method maintains the mid-level features of the convolutional layers and selectively utilizes features useful for classification. We compared the proposed method with previous methods using public and self-collected databases. The experimental results show that the equal error rate is less than 1%, which is superior to the previous methods. In addition, we propose a new method to analyze whether mid-stage features have been utilized. As a result, it was confirmed that this approach, which utilizes the mid-level features, can effectively improve the feature extraction performance of the network.