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Iris Recognition has been one of the most robust means of biometric recognition. In recent years, there are so many researches in iris recognition using deep learning. In this paper, deep neural networks are used for segmentation, feature extraction and classification. The Dense Fully Convolutional Network is used for segmentation. The segmented iris is normalized which is then converted to Gabor wavelet for feature extraction. The Gabor features are extracted using multiclass Support Vector Machine classifier. The proposed method is tested on CASIA Iris 1000 and IITD datasets. The performance of the proposed method is evaluated using accuracy, precision and recall measures. It is also compared with state-of-the-art methods.