Abstract: Cognitive load assessment is crucial for user studies and human-computer interaction designs. As a noninvasive and easy-to-use category of measures, current photoplethysmogram(PPG)-based assessment methods rely on single or small-scale predefined features to recognize responses induced by people’s cognitive load, which are not stable in assessment accuracy. In this study, we propose a machine-learning method by using 46 kinds of PPG features together to improve the measurement accuracy for cognitive load. We test the method on 16 participants through the classical n-back tasks (0-back, 1-back, and 2-back). The accuracy of the machine-learning method in differentiating different levels of cognitive loads induced by task difficulties can reach 100% in 0-back vs. 2-back tasks, which outperformed the traditional HRV-based and single-PPG-feature based methods by 12-55%. When using “leave-one-participant-out” subject-independent cross validation, 87.5% binary classification accuracy was reached which is at the state-of-the-art level. The proposed method can also support real-time cognitive load assessment by beat-to-beat classifications with better performance than the traditional single-feature-based real-time evaluation method.

Abstract: Traditional face recognition algorithms are not best suited for matching face images captured at a low resolution to a set of high-resolution gallery face images. To perform matching between face images of different resolutions, this work proposes a coupled-projection method. Based on local relationships in the data, two sets of projections are learned: one for low-resolution face images and the other for high-resolution face images, respectively. Then, both low-resolution probe face images and high-resolution gallery face images are projected into a common subspace. Hence, subsequent matching can be done in the subspace directly.