Maximum Entropy Binary Encoding for Face Template Protection
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
A Scheme for Molecular Computation of Maximum Likelihood Estimators for Log-Linear Models
We propose a novel molecular computing scheme for statistical inference. We focus on the much-studied statistical inference problem of computing maximum likelihood estimators for log-linear models. Our scheme takes log-l…
Deep Secure Encoding: An Application to Face Recognition
In this paper we present Deep Secure Encoding: a framework for secure classification using deep neural networks, and apply it to the task of biometric template protection for faces. Using deep convolutional neural networ…
Face RecognitionGeneral ClassificationMatrix Information Theory for Self-Supervised Learning
The maximum entropy encoding framework provides a unified perspective for many non-contrastive learning methods like SimSiam, Barlow Twins, and MEC. Inspired by this framework, we introduce Matrix-SSL, a novel approach t…
Contrastive LearningGSM8KLanguage ModelingLanguage Modelling+4Maximum Entropy Heterogeneous-Agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) has been shown effective for cooperative games in recent years. However, existing state-of-the-art methods face challenges related to sample complexity, training instability, and…
MuJoCoMulti-agent Reinforcement Learningreinforcement-learningReinforcement Learning+1A joint maximum-entropy model for binary neural population patterns and continuous signals
Second-order maximum-entropy models have recently gained much interest for describing the statistics of binary spike trains. Here, we extend this approach to take continuous stimuli into account as well. By constraining …