A Deep Transfer Learning Approach to Fine-Tuning Facial Recognition Models
The challenge of developing facial recognition systems has been the focus of many research efforts in recent years and has numerous applications in areas such as security, entertainment, and biometrics. Recently, most progress in this field has come from training very deep neural networks on massive datasets which is computationally intensive and time consuming. Here, we propose a deep transfer learning (DTL) approach that integrates transfer learning techniques and convolutional neural networks and apply it to the problem of facial recognition to fine-tune facial recognition models. Transfer learning can allow for the training of robust, high-performance machine learning models that require much less time and resources to produce than similarly performing models that have been trained from scratch. Using a pre-trained face recognition model, we were able to perform transfer learning to produce a network that is capable of making accurate predictions on much smaller datasets. We also compare our results with results produced by a selection of classical algorithms on the same datasets to demonstrate the effectiveness of the proposed DTL approach.
2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)
(2018). A Deep Transfer Learning Approach to Fine-Tuning Facial Recognition Models. 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA).
Available at: https://aquila.usm.edu/fac_pubs/15721