SOTAVerified

Data-specific Adaptive Threshold for Face Recognition and Authentication

2018-10-26Code Available0· sign in to hype

Hsin-Rung Chou, Jia-Hong Lee, Yi-Ming Chan, Chu-Song Chen

Code Available — Be the first to reproduce this paper.

Reproduce

Code

Abstract

Many face recognition systems boost the performance using deep learning models, but only a few researches go into the mechanisms for dealing with online registration. Although we can obtain discriminative facial features through the state-of-the-art deep model training, how to decide the best threshold for practical use remains a challenge. We develop a technique of adaptive threshold mechanism to improve the recognition accuracy. We also design a face recognition system along with the registering procedure to handle online registration. Furthermore, we introduce a new evaluation protocol to better evaluate the performance of an algorithm for real-world scenarios. Under our proposed protocol, our method can achieve a 22\% accuracy improvement on the LFW dataset.

Tasks

Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
Adience (Online Open Set)FaceNet+Adaptive ThresholdAverage Accuracy (10 times)84.3Unverified
Adience (Online Open Set)FaceNet+Fixed Threshold (0.2487)Average Accuracy (10 times)80.6Unverified
Color FERET (Online Open Set)FaceNet+Adaptive ThresholdAverage Accuracy (10 times)83.79Unverified
Color FERET (Online Open Set)FaceNet+Fixed Threshold (0.3968)Average Accuracy (10 times)80.72Unverified
LFW (Online Open Set)FaceNet+Adaptive ThresholdAverage Accuracy (10 times)76.46Unverified
LFW (Online Open Set)FaceNet+Fixed Threshold (0.3779)Average Accuracy (10 times)53.97Unverified

Reproductions