Biblio

Currently, fusing visible and thermal images for facial expression recognition requires two modalities during both training and testing. Visible cameras are commonly used in real-life applications, and thermal cameras are typically only available in lab situations due to their high price. Thermal imaging for facial expression recognition is not frequently used in real-world situations. To address this, we propose a novel thermally enhanced facial expression recognition method which uses thermal images as privileged information to construct better visible feature representation and improved classifiers by incorporating adversarial learning and similarity constraints during training. Specifically, we train two deep neural networks from visible images and thermal images. We impose adversarial loss to enforce statistical similarity between the learned representations of two modalities, and a similarity constraint to regulate the mapping functions from visible and thermal representation to expressions. Thus, thermal images are leveraged to simultaneously improve visible feature representation and classification during training. To mimic real-world scenarios, only visible images are available during testing. We further extend the proposed expression recognition method for partially unpaired data to explore thermal images' supplementary role in visible facial expression recognition when visible images and thermal images are not synchronously recorded. Experimental results on the MAHNOB Laughter database demonstrate that our proposed method can effectively regularize visible representation and expression classifiers with the help of thermal images, achieving state-of-the-art recognition performance.

Modern detection systems use sensor outputs available in the deployment environment to probabilistically identify attacks. These systems are trained on past or synthetic feature vectors to create a model of anomalous or normal behavior. Thereafter, run-time collected sensor outputs are compared to the model to identify attacks (or the lack of attack). While this approach to detection has been proven to be effective in many environments, it is limited to training on only features that can be reliably collected at detection time. Hence, they fail to leverage the often vast amount of ancillary information available from past forensic analysis and post-mortem data. In short, detection systems do not train (and thus do not learn from) features that are unavailable or too costly to collect at run-time. Recent work proposed an alternate model construction approach that integrates forensic "privilege" information–-features reliably available at training time, but not at run-time–-to improve accuracy and resilience of detection systems. In this paper, we further evaluate two of proposed techniques to model training with privileged information: knowledge transfer, and model influence. We explore the cultivation of privileged features, the efficiency of those processes and their influence on the detection accuracy. We observe that the improved integration of privileged features makes the resulting detection models more accurate. Our evaluation shows that use of privileged information leads to up to 8.2% relative decrease in detection error for fast-flux bot detection over a system with no privileged information, and 5.5% for malware classification.