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WeAreDevelopers LIVE • Dec 7, 2021

Unboxing the DeepFace

Sefik Serengil

Standardizing eye alignment horizontally improves facial recognition accuracy by over 1%. Learn how the DeepFace framework abstracts complex computer vision pipelines into just a few lines of Python.

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#1 about 5 min

Introduction to the DeepFace Python library and setup

How to install open-source DeepFace and run basic verification tasks.

#2 about 2 min

Time complexity in face verification versus face recognition

Understanding the performance difference between one-to-one verification and one-to-many database searches.

#3 about 2 min

Extracting facial demography attributes like age and gender

Analyzing apparent demography and emotions to decrease search space and fight bias.

#4 about 3 min

Five core stages of a modern face recognition pipeline

Using powerful deep learning models to locate raw faces and landmarks.

#5 about 1 min

Aligning facial features to increase model recognition accuracy

Rotating input images to horizontally align eye coordinates prior to model ingestion.

#6 about 1 min

Normalizing facial images to remove noisy background information

Extracting just the facial visual data and discarding unhelpful background pixels.

#7 about 6 min

Generating vector embeddings with convolutional neural networks

Replacing traditional classification output layers with embedding arrays to support unknown future identities.

#8 about 4 min

Verifying face pairs by calculating multidimensional vector distances

Applying distance thresholding formulas to output embeddings to determine accurate matching identities.

#9 about 3 min

Preparing highly scalable facial recognition for big data

Reducing large-scale database search spaces through fast filtering by known demographic attributes.

#10 about 3 min

Speeding up similarity lookups with approximate nearest neighbor algorithms

Using heuristic indexing algorithms instead of linear neighbor comparisons to search billions of images.

#11 about 3 min

Exploring database architectures for storing facial vector embeddings

Storing resulting vectors efficiently in dedicated key-value stores rather than standard relational databases.

#12 about 3 min

Key advantages and flexibility of the DeepFace framework

Why lightweight design without complex C plus plus dependencies enables rapid deep learning integration.

#13 about 13 min

Answering audience questions on deepfakes and identical twins

Discussions regarding emotion accuracy, deepfakes, compatible sensor inputs, and recognizing identical twins.

Matching moments

5:25 min

Technical pipeline for real-time video deepfakes

Martin Förtsch Martin Förtsch +1 · World Congress 2024

3:09 min

Defining true deepfakes generated by trained neural networks

Thomas Endres Thomas Endres +2 · World Congress 2021

2:30 min

Implementing the deepfake inference workflow for face generation

Thomas Endres Thomas Endres +2 · World Congress 2021

3:36 min

Differentiating traditional video fakes from neural network models

Thomas Endres Thomas Endres +2 · World Congress 2021

3:32 min

Implementing automated object and face recognition for images

Arto Liukkonen · LIVE

3:59 min

Preparing video datasets and extracting faces with FaceNet

Thomas Endres Thomas Endres +2 · World Congress 2021

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