Name
Abstract | ||
|---|---|---|
In this paper we present a framework for secure identification using deep neural networks, and apply it to the task of template protection for face authentication. We use deep convolutional neural networks (CNNs) to learn a mapping from face images to maximum entropy binary (MEB) codes. The mapping is robust enough to tackle the problem of exact matching, yielding the same code for new samples of a user as the code assigned during training. These codes are then hashed using any hash function that follows the random oracle model (like SHA-512) to generate protected face templates (similar to text based password protection). The algorithm makes no unrealistic assumptions and offers high template security, cancelability, and state-of-the-art matching performance. The efficacy of the approach is shown on CMU-PIE, Extended Yale B, and Multi-PIE face databases. We achieve high (~95%) genuine accept rates (GAR) at zero false accept rate (FAR) with up to 1024 bits of template security. |
Year | Venue | Field |
|---|---|---|
2015 | CoRR | Authentication,Pattern recognition,Binary encoding,Convolutional neural network,Computer science,Random oracle,Hash function,Artificial intelligence,Template,Principle of maximum entropy,Machine learning,Binary number |
DocType | Volume | Citations |
Journal | abs/1512.01691 | 0 |
PageRank | References | Authors |
0.34 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rohit Pandey | 1 | 7 | 1.83 |
| Yingbo Zhou | 2 | 263 | 19.43 |
| bhargava urala kota | 3 | 0 | 0.34 |
| Venu Govindaraju | 4 | 3521 | 422.00 |