Anurag Awasthi

Anurag Awasthi

Active Appearance Models (AAMs) are a well-established technique for fitting deformable models to images, but they are limited by linear appearance assumptions and can struggle with complex variations. In this paper, we explore if the AAM fitting process can benefit from a Generative Adversarial Network (GAN). We uses a U-Net based generator and a PatchGAN discriminator for GAN-augmented framework in an attempt to refine the appearance model during fitting. This approach attempts to addresses challenges such as non-linear appearance variations and occlusions that traditional AAM optimization methods may fail to handle. Limited experiments on face alignment datasets demonstrate that the GAN-enhanced AAM can achieve higher accuracy and faster convergence than classic approaches with some manual interventions. These results establish feasibility of GANs as a tool for improving deformable model fitting in challenging conditions while maintaining efficient performance, and establishes the need for more future work to evaluate this approach at scale.

Aniket Vaidya, Anurag Awasthi

In today's increasingly digital interactions, robust Identity Verification (IDV) is crucial for security and trust. Artificial Intelligence (AI) is transforming IDV, enhancing accuracy and fraud detection. This paper introduces ``Zero to One,'' a holistic conceptual framework for developing AI-powered IDV products. This paper outlines the foundational problem and research objectives that necessitate a new framework for IDV in the age of AI. It details the evolution of identity verification and the current regulatory landscape to contextualize the need for a robust conceptual model. The core of the paper is the presentation of the ``Zero to One'' framework itself, dissecting its four essential components: Document Verification, Biometric Verification, Risk Assessment, and Orchestration. The paper concludes by discussing the implications of this conceptual model and suggesting future research directions focused on the framework's further development and application. The framework addresses security, privacy, UX, and regulatory compliance, offering a structured approach to building effective IDV solutions. Successful IDV platforms require a balanced conceptual understanding of verification methods, risk management, and operational scalability, with AI as a key enabler. This paper presents the ``Zero to One'' framework as a refined conceptual model, detailing verification layers, and AI's transformative role in shaping next-generation IDV products.