One of the best biometrics used for human verification and identification is iris recognition. The contrast of its unique characteristics differs from one candidate to another in which the iris pattern has numerous well-known features like uniqueness texture, stability and compactness representation for human identification. Among these facts, several approaches in these areas are localized, but there is still an abundant problems such as the low match rate of the score level and low accuracy. Therefore, a decision model should be essential for iris recognition frameworks. This paper proposes a iris recognition based on a decision model using a unified framework based on the integration of three detection schemes due to variation occurred in shading and position change using Smallest Univalue Segment Assimilating Nucleus (SUSAN), Generalized Hough Transform (GHT) and Viola–Jones (SUSANGHT-VJ) for eye detection, enhancing the dimmer and darker areas using fuzzy retinex method and normalizing the iris boundary using daugman’s Rubber Sheet Model for segmentation. Also, the iris corner points is extracted using Gabor Wavelet Transform (GWT), the vector properties of the blurred texture features are quantized using Local Phase Quantization (LPQ) and the optimal decision model based on Atom Search Optimization (ASO) and Feed Forward Counter propagation Neural Network (FFCNN) for matching score level and classification task. Furthermore, the current framework prevents false matches and inappropriate iris input, thus making the iris match score framework more reliable. The evaluation of the proposed approach is trained and tested with the employed eye template of iris and face datasets. Therefore, the results depict that the proposed technique gives a high recognition rate of 99.9% on different datasets compared to existing methods.

用于人类验证和识别的最佳生物识别技术之一是虹膜识别。其独特特征的对比因一个候选者而异，其中虹膜图案具有许多众所周知的特征，例如用于人类识别的唯一性纹理，稳定性和紧凑性表示。在这些事实中，这些区域中的几种方法是局部的，但是仍然存在大量问题，例如得分水平的匹配率低和准确性低。因此，决策模型对于虹膜识别框架而言必不可少。本文提出了一种基于决策模型的虹膜识别方法，该模型使用统一框架，该框架基于三种检测方案的集成，这是由于使用最小均值段同化核（SUSAN）产生了阴影变化和位置变化而导致的，使用通用霍夫变换（GHT）和中提琴-琼斯（SUSANGHT-VJ）进行眼睛检测，使用模糊retinex方法增强暗淡区域和较暗区域，并使用daugman的Rubber Sheet Model进行虹膜边界归一化进行分割。此外，虹膜角点使用Gabor小波变换（GWT）提取，模糊纹理特征的矢量属性使用局部相位量化（LPQ）进行量化，并且基于原子搜索优化（ASO）和前馈计数器的最佳决策模型传播神经网络（FFCNN），用于匹配评分级别和分类任务。此外，当前框架防止错误匹配和不适当的虹膜输入，从而使虹膜匹配得分框架更可靠。使用虹膜和脸部数据集的眼睛模板对提出的方法的评估进行训练和测试。因此，结果表明，与现有方法相比，该技术在不同数据集上的识别率高达99.9％。