Iris Recognition Method Based on Parallel Iris Localization Algorithm and Deep Learning Iris Verification

Abstract

Biometric recognition technology has been widely used in various fields of society. Iris recognition technology, as a stable and convenient biometric recognition technology, has been widely used in security applications. However, the iris images collected in the actual non-cooperative environment have various noises. Although mainstream iris recognition methods based on deep learning have achieved good recognition accuracy, the intention is to increase the complexity of the model. On the other hand, what the actual optical system collects is the original iris image that is not normalized. The mainstream iris recognition scheme based on deep learning does not consider the iris localization stage. In order to solve the above problems, this paper proposes an effective iris recognition scheme consisting of the iris localization and iris verification stages. For the iris localization stage, we used the parallel Hough circle to extract the inner circle of the iris and the Daugman algorithm to extract the outer circle of the iris, and for the iris verification stage, we developed a new lightweight convolutional neural network. The architecture consists of a deep residual network module and a residual pooling layer which is introduced to effectively improve the accuracy of iris verification. Iris localization experiments were conducted on 400 iris images collected under a non-cooperative environment. Compared with its processing time on a graphics processing unit with a central processing unit architecture, the experimental results revealed that the speed was increased by 26, 32, 36, and 21 times at 4 different iris datasets, respectively, and the effective iris localization accuracy is achieved. Furthermore, we chose four representative iris datasets collected under a non-cooperative environment for the iris verification experiments. The experimental results demonstrated that the network structure could achieve high-precision iris verification with fewer parameters, and the equal error rates are 1.08%, 1.01%, 1.71%, and 1.11% on 4 test databases, respectively.

A multimodal biometric database and case study for face recognition based deep learning

“…Statistical analysis performed in found [22], iris possesses the most reliable and constant features of all biological qualities. Consequently, some recent study employing the iris trait [23], [24]. As a result, we provide an iris database in MULBv1.…”

Robust iris localization algorithm based on improved YOLOv4 and self-organized particle swarm optimization

“…Iris recognition has the advantages of uniqueness, stability, non-contact and anti-counterfeiting [1]~ [4] , and has a good development prospect in personal identity recognition [5] . Iris localization is an important part of iris recognition.…”

An Efficient Iris Recognition Technique using CNN and Vision Transformer

“…The iris recognition system has been developed using a variety of technologies. The most common is Convolutional Neural Network (CNN) that become state of the art in image recognition [12]. However, Self-attentive mechanisms-based Vision Transformers (ViT) have successfully classified and recognized images accuracy comparable to neural networks.…”