Denoising autoencoder for iris recognition in noncooperative environments

Abstract

The iris is considered as the most unique phenotype feature visible in a person’s face and has been explored in the last three decades. Outstanding approaches are known for iris recognition task when the image is acquired in a well controlled environment. However, the problem is still challenging in a noncooperative environment. Having this context in mind, and from a learning representation perspective, in this paper, we propose the use of denoising autoencoders networks to create descriptors to iris recognition.We extract features from six regions of the iris and also use a specific scheme in the literature that employ a set of thresholds for iris acceptance/rejection. We perform experiments on two well-know databases, by comparing our descriptor with 2D Gabor and Wavelet representations of implementations of us. In both data sets, the proposed descriptor outperforms these features, and presents comparable results with the best performing method in a NICE contest.