AI-driven professional with 2+ years of experience in BFSI technical consulting and a Master’s in AI & Business Strategy from the UK. Skilled in translating business needs into AI solutions, managing end-to-end projects, and leveraging tools like Python, BCMS, and Agile frameworks. Passionate about building AI products that optimize workflows, reduce risk, ensuring accuracy and minimal errors in deliverables. Known for problem-solving abilities, attention to detail, and a passion for innovation. Committed to continuous learning and staying updated on the latest technology trends to drive impactful digital transformation.
Enhancing Player Modelling using FER leveraging DDA guided LLM in Video Games.
This project presents Emotion-Adaptive Street Fighter, a two-dimensional fighting game designed to explore the integration of real-time emotion recognition into dynamic difficulty adjustment (DDA) systems and using GEMINI API services to detect sentimental analysis . The core innovation lies in the game's ability to adapt its difficulty based on the player’s emotional state, as inferred from facial expressions. Utilizing a webcam interface, the system captures and analyses facial cues through computer vision and deep learning algorithms DeepFace Framework. Gameplay parameters are then adjusted dynamically reducing difficulty when the player appears frustrated or angry, and increasing it when expressions of satisfaction or enjoyment are detected. The aim is to maintain optimal engagement, reduce potential frustration, and enhance the overall gaming experience. Serving both as an entertainment platform and a research tool, Emotion-Adaptive Street Fighter offers empirical insights into the impact of emotion-aware DDA on player satisfaction, engagement, and emotional immersion in interactive digital environments.