  With the increase in computation power and the development of new
state-of-the-art deep learning algorithms, appearance-based gaze estimation is
becoming more and more popular. It is believed to work well with curated
laboratory data sets, however it faces several challenges when deployed in real
world scenario. One such challenge is to estimate the gaze of a person about
which the Deep Learning model trained for gaze estimation has no knowledge
about. To analyse the performance in such scenarios we have tried to simulate a
calibration mechanism. In this work we use the MPIIGaze data set. We trained a
multi modal convolutional neural network and analysed its performance with and
without calibration and this evaluation provides clear insights on how
calibration improved the performance of the Deep Learning model in estimating
gaze in the wild.
