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Thrupthi Ann John

Thrupthi Ann John supervised by Prof.  Jawahar C V received  her doctorate in  Computer Science and Engineering (CSE). Here’s a  summary of her research work on Interpretation and Analysis of Deep Face Representations: Methods and Applications:

The rapid growth of deep neural network models in the face domain has led to their adoption in safety-critical applications. However, a crucial limitation hindering their widespread deployment is the lack of comprehensive understanding of how these models work and the inability to explain their decisions. Explainability is essential for ensuring the correctness, reliability, and fairness of AI systems, and there is a growing recognition of its importance across AI applications. Despite the significance of explainability, most current methods are designed for general object recognition tasks and cannot be directly applied to the face domain. Faces are highly structured objects, and face tasks often involve fine-grained details, making them unique and distinct from general object recognition. This thesis aims to bridge the gap in explainability literature for the face domain by providing novel methods for interpreting and analyzing deep face representations. In this thesis, we embark on a comprehensive journey of interpreting and analyzing deep face representations to uncover the underlying mechanisms behind DNN-based face-processing models. We first visualize face representations and introduce methods to identify functional concepts in face representations using ’cross-task aware filters’ (CRAFT). Our approach includes an efficient task-aware pruning method using CRAFTs. We also present state-of-the-art Canonical Saliency Maps (CMS) to pinpoint critical input features. We thoroughly analyze deep face representations to understand the learned features and their functional relevance in different face tasks. To further enhance our understanding of human attention in the context of driving behavior, we investigate driver gaze patterns and develop DashGaze, a large-scale naturalistic driver gaze dataset . Using this dataset, we propose an innovative calibration-free driver gaze estimation algorithm that provides valuable information for studying and predicting driver behavior. The comprehensive overview, experimental studies, and analyses presented in this thesis contribute to the wider adoption of explainability methods in face-processing tasks, enabling safer and more trustworthy deployment of deep-face algorithms in real-world applications. By shedding light on the inner workings of these models and their biases, this work paves the way for the responsible and ethical development of AI technologies in the face domain.

 

December 2024