With the improvement of hardware, deep learning features overcome the weakness of traditional man-made feature such as poor robustness and complex retrieval calculation.A coarse-to-fine face image retrieval based on deep learning feature was proposed.First, a face feature extraction model is developed by using nearly four million face images to train the convolutional neural networks.Second, face feature is extracted, stored and clustered.Finally, face retrieval is performed by coarse-to-fine retrieval.Face verification gets a 99.1% accuracy via deep learning face feature in the LFW benchmark and face retrieval costs only about 0.5 second in a million face retrieval benchmark.The experiment results illustrate that deep learning face feature is more robust and lower in computational complexity.The retrieval method from coarse to fine has high efficiency and high accuracy.