Many researchers have discovered FaceReader as a tool for their research. These 3 recent studies with FaceReader show how emotion data helps you to better understand human-human, human-machine, and human-product interactions.
As emotions run through everyday life, facial expression analysis is often used in consumer and behavior research. With FaceReader 7.1 you can now detect affective attitudes as well.
What does ‘nice’ actually mean in relation to psychological variables? And does it positively correlate with self-reported levels of health, happiness, and wellbeing?
Many researchers are interested in the relationship between facial expressions and music preference. This is the first study that uses FaceReader to investigate this.
José Chavaglia Neto and José António Filipe investigated the effect of one commercial on consumer emotion. They asked consumers to watch this commercial related to a specific brand.
Are you interested in using automatic facial expression analysis in a standardized lab setting? Here are 5 tips to get you started!
We all have our favorite celebrities, then there are those we love to hate. Who would you want to pay to represent your product or brand?
Real time measurement techniques like FaceReader might be the key to measuring flow in real-time.
David Schindler and colleagues developed a software, µCap (muCap), which is capable of creating a link between video footage and phases of the experiment, suitable for automated analysis in FaceReader.
Are you involved in emotion recognition and facial expression analysis? These 5 tips will guarantee the best results!