06-16, 10:15–10:55 (Europe/London), Minories
This talk explores the symbiotic relationship between art and science in facial recognition technology. Focusing on the DeepFace library in Python, the talk unravels the intricacies of deep learning algorithms that breathe life into pixels, enabling computers to artistically recognize and interpret human faces. Attendees will witness practical implementations, gaining insights into the ethical considerations surrounding this transformative technology, and ultimately understanding the harmonious blend of art and science at the core of deep face recognition.
Join us for a captivating exploration of "Behind the Pixels: The Art and Science of Deep Face Recognition in Python." In this talk, we embark on a journey into the heart of facial recognition, where artistry and science converge seamlessly. Focusing on the DeepFace library in Python, we'll demystify the intricate neural networks that enable computers to decipher and recognize human faces. Through practical demonstrations, participants will witness the real-world applications of this technology, from biometric security to personalized user experiences. Delving into ethical considerations, we'll ponder the societal impact of deep face recognition. Whether you're a developer, tech enthusiast, or simply curious about the transformative power of facial recognition, this talk offers a unique perspective on the harmonious interplay between art and science in the world of pixels and faces.
No previous knowledge expected
This is Sefik.
I received my MSc in Computer Science from Galatasaray University in 2011.
I have been working as a software engineer since 2010.
Currently, I live and work in the financial district in London, UK but I am a true Istanbulite at heart.
My research interests are Machine Learning and Cryptography. I’ve published several research papers about these motivations. Also, I enjoy speaking to communities about these disciplines.
Moreover, I enjoy to contribute open source projects. Explore my GitHub repository to discover some notable projects, including DeepFace—a lightweight face recognition and facial attribute analysis library for Python, boasting 9K stars; RetinaFace—an lightweight Deep Face Detection Library for Python with 854 stars; and ChefBoost—a lightweight boosted decision tree library for Python, celebrated with 431 stars.