This paper delves into the challenging task of selfie vs ID face verification which involves matching high-resolution selfies with low-resolution faces extracted from scanned ID documents. Existing face verification models often face performance degradation when confronted with this task, mainly due to disparities in data distributions, such as age-difference, degradation due to scanning, and difference in appearance. To address this issue and enhance performance, the paper explores the implementation of facial quality assessment and hard-pair mining techniques. In addition, the paper investigates the potential of synthetic data for training face verification models tailored for this specific task. The integration of synthetic data as an alternative training source is explored to improve robustness and overcome legal and privacy concerns arising from authentic datasets. By combining hard pair mining, facial quality assessment, and the utilization of synthetic data, this paper presents a comprehensive framework that aims to achieve improved face verification results in the complex scenario of selfie vs ID matching. The goal is to optimize the models’ performance and enhance their ability to accurately match selfies with the corresponding ID images, even under challenging conditions.

Perceiving text is crucial to understand semantics of outdoor scenes and hence is a critical requirement to build intelligent systems for driver assistance and self-driving. Most of the existing datasets for text detection and recognition comprise still images and are mostly compiled keeping text in mind. This paper introduces a new "RoadText-1K" dataset for text in driving videos. The dataset is 20 times larger than the existing largest dataset for text in videos. Our dataset comprises 1000 video clips of driving without any bias towards text and with annotations for text bounding boxes and transcriptions in every frame. State of the art methods for text detection, recognition and tracking are evaluated on the new dataset and the results signify the challenges in unconstrained driving videos compared to existing datasets. This suggests that RoadText-1K is suited for research and development of reading systems, robust enough to be incorporated into more complex downstream tasks like driver assistance and self-driving. The dataset can be found at http://cvit.iiit.ac.in/research/projects/cvit-projects/roadtext-1k.