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Labelling of human behavior analysis data is a complex and time consumingtask. In this paper, a fully automatic technique for labelling an image basedgaze behavior dataset for driver gaze zone estimation is proposed. Domainknowledge is added to the data recording paradigm and later labels aregenerated in an automatic manner using Speech To Text conversion (STT). Inorder to remove the noise in the STT process due to different illumination andethnicity of subjects in our data, the speech frequency and energy areanalysed. The resultant Driver Gaze in the Wild (DGW) dataset contains 586recordings, captured during different times of the day including evenings. Thelarge scale dataset contains 338 subjects with an age range of 18-63 years. Asthe data is recorded in different lighting conditions, an illumination robustlayer is proposed in the Convolutional Neural Network (CNN). The extensiveexperiments show the variance in the dataset resembling real-world conditionsand the effectiveness of the proposed CNN pipeline. The proposed network isalso fine-tuned for the eye gaze prediction task, which shows thediscriminativeness of the representation learnt by our network on the proposedDGW dataset. Project Page:https://sites.google.com/view/drivergazeprediction/home

Speak2Label: Using Domain Knowledge for Creating a Large Scale Driver Gaze Zone Estimation Dataset