# Unsteady State Lightweight Iris Certification Based on Multi-Algorithm Parallel Integration
## 4 Experiments and Analysis
### Certification Structure of Comprehensive Effect Experiment
#### 4.4.2 Existing Method Comparison Experiment
However, compared with the multi-state iris certification, the Hamming distance is less inclusive for the environment and is not suitable for the iris that has a large change in the iris state. Therefore, the recognition accuracy is lower in the multi-state iris. As a common deep learning architecture, convolutional neural network is commonly used in various biometric recognitions, such as face recognition [54], palmprint recognition [55], fingerprint recognition [56], and iris recognition [57]. The feature cognition of the traditional convolutional neural network is to express the iris category by setting the label. This type of method makes the iris representative, but it is affected by unsteady iris state. Such labels are very unstable under lightweight data, so the recognition accuracy is unstable. Both the VGG16 model and the Inception model are currently the most commonly used convolutional neural network models, which show very good performance in feature certification; however, this does not mean that they are suitable for the iris data in this paper. The VGG16 model is composed of 13 convolutional layers and 3 fully connected layers. In the iris recognition, the iris image is continuously convolved, pooled, and ReLU processed to obtain different types of feature values, thereby achieving iris recognition. However, due to the prerequisites in this paper, the state of iris itself is unpredictable. It can be seen from the different iris states in Figure 1. In cases where iris feature extraction can be performed, without the detailed classification of iris training data and a large amount of iris data, this unpredictable state makes it difficult to take advantage of the superiority of the VGG16 model. In addition, the amount of training data in this paper is not much. The calculation of VGG16 model is redundant, and the excessive parameters increase the difficulty of training. These greatly reduce the certification accuracy of VGG16 model. The Faster R-CNN Inception Resnet V2 model architecture is a more advanced convolutional neural network model that allows training on lightweight data compared to the VGG16 model, reducing the difficulty of training. However, under the prerequisites of this paper, this paper first adopts a variety of image processing algorithms to improve the connection between the image and the feature extraction algorithm, so that the conventional algorithm can achieve high recognition effect in the feature image. Although the R-CNN model reduces the complexity of training, it also counts the redundant features in the training. Compared with the stable features of this paper, the interference is formed. Considering the changes in the state of the iris acquisition and the conditions of the existing architecture, configure the corresponding image processing algorithm or directly input the stable features of the iris into the existing architecture may result in incompatibility between the image data and the architecture. These all reduce the certification accuracy of the model. Therefore, it can be seen that the algorithm uses the connection between the layers to effectively link the image processing, feature extraction and iris certification. The problem of incompatibility between existing architecture requirement data and image processing can be avoided, and the potential of traditional algorithms can also be fully utilized. At the same time, it satisfies the training of lightweight data in a complex and variable acquisition state.