Object and Face Recognition: From Cognitive Models to Computer Vision

How do our brains instantly recognize a friend's face in a crowd, and how can we program computers to do the same? This text-based course bridges the gap between cognitive science and artificial intelligence, exploring how biological and artificial systems process visual information. You will progress from understanding basic visual pathways to analyzing modern computer vision algorithms. By studying the core representation problem—how 2D images encode 3D objects—you will gain a solid foundation in both human perception and machine intelligence. What you'll learn: - Understand visual representation theories and how the human brain encodes 3D objects from 2D visual inputs. - Explore the cognitive and neural mechanisms behind human face recognition, including specialized brain regions. - Analyze classical computational models of object recognition and contrast them with modern deep learning approaches. - Examine the mechanics of facial detection and alignment algorithms used in modern computer vision. - Discuss contemporary challenges in machine vision, including algorithmic bias, privacy, and ethical considerations. The course begins with foundational concepts in visual perception and biological vision before transitioning into computational models, feature extraction, and modern neural network architectures. You will read through detailed explanations, conceptual breakdowns, and illustrative code blocks designed to solidify your understanding. This course is designed for beginners interested in computer vision, cognitive science, or artificial intelligence, with no prior background in neuroscience or advanced programming required. Start reading today to unlock the secrets of visual perception in both humans and machines.