LAUSR

For large-scale iris recognition tasks, the determination of classification thresholds remains a challenging task, especially in practical applications where sample space is growing rapidly. Due to the complexity of iris samples, the classification threshold is difficult to determine with the increase of samples. The key issue to solving such threshold determination problems is to obtain iris feature vectors with more obvious discrimination. Therefore, we train deep convolutional neural networks based on a large number of iris samples to extract iris features. More importantly, an optimized center loss function referred to Tight Center ( $\mathcal {T}$ -Center) Loss is used to solve the problem of insufficient discrimination caused by the traditional Softmax loss function. In order to evaluate the effectiveness of our proposed method, cosine similarity is used to estimate the similarity between the features on the published iris recognition datasets ND-IRIS-0405, CASIA-Thousand and IITD. Our experiment results prove that the $\mathcal {T}$ -Center loss can minimize intra-class variance and maximize inter-class variance, which achieve significant performance on the benchmark experiments.