<b>Performance of Hasty and Consistent Multi Spectral Iris Segmentation using Deep Learning</b> The recognition system is composed of seven phases acquisition, preprocessing, segmentation, normalization, feature extraction, feature selection, and classification. In the acquisition phase, iris images are captured, followed by preprocessing to enhance the quality of the images. The segmentation phase involves separating the iris region from the background, and the normalized iris region is shaped into a rectangle in the normalization phase. Iris segmentation is a critical step in iris recognition systems and has a direct impact on authentication and recognition results. However, standard segmentation techniques may not perform well in noisy iris databases captured under challenging conditions. Moreover, the lack of large iris databases hinders the performance improvement of convolution neural networks. The proposed method addresses these challenges by effectively handling irregular iris images captured under visible light. The iris region is processed and evaluated to generate a unique feature vector, which is then used for person identification. VGG16, a well known deep learning model, is employed for image classification, and the feature vector is fed into VGG16 for classification purposes. Deep Learning, Multi Spectral Iris, neural networks, VGG16 11-15 Issue-5 Volume-7 Ram Niwas Sharma | Ankit Kumar Navalakha | Neha Sharma