Multi Source Heterogeneous Iris Recognition Using Stacked Convolutional Deep Belief Networks Deep Belief Network Model
Abstract With the development of iris recognition technology, sensors of iris images acquisition are being constantly developed and updated. Re register users every time a new sensor is deployed is time consuming and complicated, especially in applications with large scale registered users. Therefore, it is a challenging problem to choose the common recognition model which is effective for multi source heterogeneous iris recognition(MSH IR) The paper proposes a efficient neural network model of stacked Convolutional Deep Belief Networks Deep Belief Network (CDBNs DBN) for MSH IR. The main improvements are two parts: firstly, this model uses the region by region extraction method and positions the convolution kernel through the offset of the hidden layer to locate the effective local texture feature structure. Secondly, the model uses DBN as a classifier in order to reduce the reconstruction error through the negative feedback mechanism of the auto encoder. Experimental results have been implemented on publicly available IIT Delhi iris database, which is captured by three different iris captured sensors. Experiments shows the model performs strong robustness performance and recognition ability.