Face recognition using fine-tuning of Deep Convolutional Neural Network and transfer learning

Deep learning is one of the most important scopes of the Machine Learning that includes some important architectures. Deep Convolutional Neural Network is one of the attractive architectures that uses in digital image processing. In this paper, we use the Alexnet model for face recognition from input images. We fine-tune the Alexnet model by converting one or two fully connected layers to convolutional layers as well as using the suitable filters. To improve the robustness of the model in coping with the situations that some parts of the input images damaged, we use five crops of the input images including five pixel areas. Furthermore, to visualize the output of each layer, we use the Deconvolution technique in our method. The output of some convolutional and activation layers has been shown. Using this technique, we obtain the Heat-map of the image. To show the results, we use the LFW and Caltech faces datasets. After pre-processing the images of datasets, we compare the results of the Alexnet model in two states: before fine-tuning and after fine-tuning. The results show the recognition accuracy improvement of the fine-tuned models on input images.