Sefik Serengil

Unboxing the DeepFace

How do you search billions of faces in milliseconds? This talk unboxes the open-source library and modern algorithms that make it possible.

Unboxing the DeepFace
#1about 4 minutes

Introducing the DeepFace facial recognition library

Learn about the open-source DeepFace Python library, its key features, and how to install and use it for basic face verification.

#2about 2 minutes

Understanding face verification versus face recognition

Face verification is a one-to-one comparison with O(1) complexity, while face recognition is a one-to-many search with O(n) complexity.

#3about 2 minutes

Analyzing facial attributes like age and gender

DeepFace can predict apparent age, gender, emotion, and race to help reduce search space or mitigate dataset bias.

#4about 2 minutes

Stage 1: Detecting faces with different backends

Choose from various face detectors like OpenCV for speed or RetinaFace for higher accuracy in crowded images.

#5about 2 minutes

Stages 2 & 3: Aligning and normalizing facial images

Improve accuracy by rotating images to align the eyes horizontally and cropping the facial area to remove background noise.

#6about 6 minutes

Stage 4: Creating vector embeddings with neural networks

Convolutional neural networks convert facial images into unique vector embeddings, avoiding the need to retrain the model for new identities.

#7about 3 minutes

Stage 5: Verifying identity using distance and thresholds

Calculate the distance between two vector embeddings to determine if they represent the same person by comparing it to a pre-defined threshold.

#8about 3 minutes

The challenge of scaling facial recognition to billions of images

Traditional one-to-many search is too slow for large-scale applications like those at Google or Facebook, requiring more advanced algorithms.

#9about 2 minutes

Using Approximate Nearest Neighbor for fast searching

Accelerate large-scale searches by using Approximate Nearest Neighbor (ANN) algorithms, which trade perfect accuracy for significant speed gains.

#10about 3 minutes

Choosing the right tech stack for your use case

Select key-value stores like Redis for fast verification, distributed systems like Spark for high-confidence recognition, or vector databases for ANN-powered search.

#11about 3 minutes

Key benefits of using the DeepFace library

DeepFace is a lightweight, easy-to-install, open-source library that wraps state-of-the-art models and is language-independent via its API.

#12about 13 minutes

Audience Q&A on emotion detection, 3D sensors, and bias

The speaker answers questions about measuring emotion, handling 3D data, determining thresholds, and addressing bias in training datasets.

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Matching moments

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13:05 MIN

How traditional deepfakes are made with Deep Face Lab

Deepfakes in Realtime - How Neural Networks Are Changing Our World

The technical process behind creating real-time deepfakes
04:34 MIN

The technical process behind creating real-time deepfakes

The AI Elections: How Technology Could Shape Public Sentiment

Showcasing computer vision project examples
03:09 MIN

Showcasing computer vision project examples

Computer Vision from the Edge to the Cloud done easy

Introducing the Focoos AI developer platform
00:57 MIN

Introducing the Focoos AI developer platform

Focoos AI: Building the Future of Computer Vision

Building a real-time deepfake pipeline
09:33 MIN

Building a real-time deepfake pipeline

Deepfakes in Realtime - How Neural Networks Are Changing Our World

Differentiating fakes from true deepfakes
03:56 MIN

Differentiating fakes from true deepfakes

Deepfakes in Realtime - How Neural Networks Are Changing Our World

Building applications like intrusion and face detection
03:57 MIN

Building applications like intrusion and face detection

What comes after ChatGPT? Vector Databases - the Simple and powerful future of ML?

Applying AI and database technology in FinTech
05:17 MIN

Applying AI and database technology in FinTech

OpenAI for FinTech: Building a Stock Market Advisor Chatbot

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