Name
Title | ||
|---|---|---|
Machine Learning VS Transfer Learning Smart Camera Implementation for Face Authentication. |
Abstract | ||
|---|---|---|
The aim of this paper is to highlight differences between classical machine learning and transfer learning applied to low cost real-time face authentication. Furthermore, in an access control context, the size of biometric data should be minimized so it can be stored on a remote personal media. These constraints have led us to compare only lightest versions of these algorithms. Transfer learning applied on Mobilenet v1 raises to 85% of accuracy, for a 457Ko model, with 3680s and 1.43s for training and prediction tasks. In comparison, the fastest integrated method (Random Forest) shows accuracy up to 90% for a 7,9Ko model, with a fifth of a second to be trained and a hundred of microseconds for the prediction, enabling embedded real-time face authentication at 10 fps. |
Year | Venue | Field |
|---|---|---|
2018 | ICDSC | Authentication,Computer science,Transfer of learning,Smart camera,Real-time computing,Access control,Artificial intelligence,Biometric data,Random forest,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
2 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pierre Bonazza | 1 | 0 | 0.34 |
| Johel Mitéran | 2 | 0 | 0.68 |
| Dominique Ginhac | 3 | 105 | 17.27 |
| Julien Dubois | 4 | 146 | 18.76 |