  User independent emotion recognition with large scale physiological signals
is a tough problem. There exist many advanced methods but they are conducted
under relatively small datasets with dozens of subjects. Here, we propose
Res-SIN, a novel end-to-end framework using Electrodermal Activity(EDA) signal
images to classify human emotion. We first apply convex optimization-based EDA
(cvxEDA) to decompose signals and mine the static and dynamic emotion changes.
Then, we transform decomposed signals to images so that they can be effectively
processed by CNN frameworks. The Res-SIN combines individual emotion features
and external emotion benchmarks to accelerate convergence. We evaluate our
approach on the PMEmo dataset, the currently largest emotional dataset
containing music and EDA signals. To the best of author's knowledge, our method
is the first attempt to classify large scale subject-independent emotion with
7962 pieces of EDA signals from 457 subjects. Experimental results demonstrate
the reliability of our model and the binary classification accuracy of 73.65%
and 73.43% on arousal and valence dimension can be used as a baseline.
