  Labelling of human behavior analysis data is a complex and time consuming
task. In this paper, a fully automatic technique for labelling an image based
gaze behavior dataset for driver gaze zone estimation is proposed. Domain
knowledge is added to the data recording paradigm and later labels are
generated in an automatic manner using Speech To Text conversion (STT). In
order to remove the noise in the STT process due to different illumination and
ethnicity of subjects in our data, the speech frequency and energy are
analysed. The resultant Driver Gaze in the Wild (DGW) dataset contains 586
recordings, captured during different times of the day including evenings. The
large scale dataset contains 338 subjects with an age range of 18-63 years. As
the data is recorded in different lighting conditions, an illumination robust
layer is proposed in the Convolutional Neural Network (CNN). The extensive
experiments show the variance in the dataset resembling real-world conditions
and the effectiveness of the proposed CNN pipeline. The proposed network is
also fine-tuned for the eye gaze prediction task, which shows the
discriminativeness of the representation learnt by our network on the proposed
DGW dataset. Project Page:
https://sites.google.com/view/drivergazeprediction/home
