MMSP 2019, IEEE 21st International Workshop on Multimedia Signal Processing, 27-29 September 2019, Kuala Lumpur, Malaysia
In this paper, we propose a secure visual-thermal fused face recognition system using non-linear hashing. To extract features from both thermal and visible facial images, a deep neural network model pre-trained by visible images, namely InsightFace, is utilized in extracting deep features from both thermal and visible images. Next, we investigate into the effectiveness of using nonlinear hashing in protecting deep features extracted from both thermal and visible face images. To further boost the accuracy performance of the facial recognition system under unfavorable environment, feature- and score-level fusion of thermal and visible images for face matching are studied. The performance of different application scenarios are tested on the EURECOM VIS-TH face dataset. Experiment results suggest that: 1) feature- and score-level fusion techniques are effective
in achieving higher accuracy under unfavorable situation; 2) non-linear hashing offers additional layer of protection, namely, privacy preservation, to face image. We also found that the deep model trained by using visible images is applicable to thermal images for feature extraction, which is particularly useful because there is no large thermal dataset available to train deep neural network
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