Recognizing hidden emotions from difference image using mean local mapped pattern

Recent progress in computer vision has pushed the limit of facial recognition from human identification to micro-expressions (MEs). However, the visual analysis of MEs is still a very challenging task because of the short occurrence and insignificant intensity of the underlying signals. To date, the accuracy of recognizing hidden emotions from frames using conventional methods is still far from reaching saturation. To address this, we have proposed a new ME recognition approach based on Mean Local Mapped Pattern (M-LMP) as a texture feature, which outperforms other state-of-the art features in terms of accuracy due to its capability of capturing small pixel transitions. Inspired by previous work, we applied M-LMP to the difference image computed from an onset frame and an apex frame, where the former represents the frame with neutral emotion and the latter consists of the frame with the largest ME intensity. The extracted local features were classified using support vector machine (SVM) and K nearest neighbourhood (KNN) classifiers. The validation of the proposed approach was performed on the CASME II and CAS(ME)2 datasets, and the results were compared with other similar state-of-the-art approaches. Comprehensive experiments were conducted using various parameters to show the robustness of our approach in the imbalanced and small dataset

Measuring the performance of visual to auditory information conversion.

BACKGROUND: Visual to auditory conversion systems have been in existence for several decades. Besides being among the front runners in providing visual capabilities to blind users, the auditory cues generated from image sonification systems are still easier to learn and adapt to compared to other similar techniques. Other advantages include low cost, easy customizability, and universality. However, every system developed so far has its own set of strengths and weaknesses. In order to improve these systems further, we propose an automated and quantitative method to measure the performance of such systems. With these quantitative measurements, it is possible to gauge the relative strengths and weaknesses of different systems and rank the systems accordingly. METHODOLOGY: Performance is measured by both the interpretability and also the information preservation of visual to auditory conversions. Interpretability is measured by computing the correlation of inter image distance (IID) and inter sound distance (ISD) whereas the information preservation is computed by applying Information Theory to measure the entropy of both visual and corresponding auditory signals. These measurements provide a basis and some insights on how the systems work. CONCLUSIONS: With an automated interpretability measure as a standard, more image sonification systems can be developed, compared, and then improved. Even though the measure does not test systems as thoroughly as carefully designed psychological experiments, a quantitative measurement like the one proposed here can compare systems to a certain degree without incurring much cost. Underlying this research is the hope that a major breakthrough in image sonification systems will allow blind users to cost effectively regain enough visual functions to allow them to lead secure and productive lives

Average Information Lost During Conversion.

<p>Average Information Lost During Conversion.</p

Pearson Correlation of IID & ISD.

<p>Pearson Correlation of IID & ISD.</p

Correlation of Prototype 3.

<p>Scatter plot that shows correlation between Inter Image Distance and Inter Sound Distance for Prototype 3.</p

Correlation of Prototype 2.

<p>Scatter plot that shows correlation between Inter Image Distance and Inter Sound Distance for Prototype 2.</p

Correlation of Prototype 1.

<p>Scatter plot that shows correlation between Inter Image Distance and Inter Sound Distance for Prototype 1.</p