The iris images collected by iris recognition system used for smart mobile devices are seriously interfered under visible light. The interference reduces recognition accuracy and decreases the robustness. An iris recognition method based on analogous convolutional neural network and local feature extraction is proposed. Firstly, a haze removal algorithm using dark channel is utilized to enhance the iris texture and reduce the light interference. Then, the analogous convolutional neural network is used to reduce the image dimension, and the texture information is obtained with a binary image. The feature vector of the iris image is built by local feature extraction of the regions of the lower dimension image. Finally, Euclidean distance is utilized in matching process. To validate the performance of the proposed method, the 30 people’s 240 iris images (four indoor images for each person and four outdoor images for each person) in MICHEI iris gallery are tested, and a comparison is conducted with iris recognition methods of Gabor transform and Principal Component Analysis (PCA). The results show that the recognition accuracy under the condition of both indoor and outdoor images can reach 98.33% and the approach has better robustness under the indoor and outdoor light interference. The performance is superior to the Gabor transform and PCA methods. These demonstrate that the proposed method can satisfy the requirements of iris recognition on mobile device.