Sefik Serengil
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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