A deep learning approach for iris sensor model identification

The aim of this paper is to propose an algorithm based on convolutional neural networks (CNN) for iris sensor model identification. This task is important in forensics applications as well as to face the problem of sensor interoperability in large scale systems. When different sensor models are involved in a recog- nition system, in fact, the overall performance can strongly decrease. A possible solution consists in first identifying the sensor model and then mapping the features extracted from the image from one sensor to the other. To keep low both complexity and memory requirements we propose a simple network archi- tecture and the use of transfer learning to speed-up the training phase and tackle the problem of limited training set availability. Experiments are carried out on several public iris databases. First, we show that the proposed solution outperforms the state-of-the art approaches used for the model identification task. Then, we test the performance of a biometric recognition system and show that improving the sensor model identification step can benefit the iris sensor interoperability.