From 40ff7c823c1ccb026c1e638baba7bcb20864e19c Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Thu, 17 Jan 2019 16:31:25 +0100 Subject: pyramids=2 --- megapixels/app/server/api.py | 2 +- megapixels/app/server/tasks/demo.py | 2 +- site/public/datasets/lfw/index.html | 15 ++------------- 3 files changed, 4 insertions(+), 15 deletions(-) diff --git a/megapixels/app/server/api.py b/megapixels/app/server/api.py index b3bce9bc..61789fb9 100644 --- a/megapixels/app/server/api.py +++ b/megapixels/app/server/api.py @@ -66,7 +66,7 @@ def upload(dataset_name): detector = face_detector.DetectorDLIBHOG() # get detection as BBox object - bboxes = detector.detect(im_np, largest=True) + bboxes = detector.detect(im_np, largest=True, pyramids=2) if not bboxes or not len(bboxes): return jsonify({ 'error': 'bbox' diff --git a/megapixels/app/server/tasks/demo.py b/megapixels/app/server/tasks/demo.py index f7db9034..12d83383 100644 --- a/megapixels/app/server/tasks/demo.py +++ b/megapixels/app/server/tasks/demo.py @@ -81,7 +81,7 @@ def demo_task(self, uuid_name, fn): face_detector_instance = face_detector.DetectorDLIBCNN(gpu=opt_gpu) # -1 for CPU step('Detecting face') st = time.time() - bboxes = face_detector_instance.detect(im_resized, largest=True) + bboxes = face_detector_instance.detect(im_resized, largest=True, pyramids=2) bbox = bboxes[0] dim = im_resized.shape[:2][::-1] bbox_dim = bbox.to_dim(dim) diff --git a/site/public/datasets/lfw/index.html b/site/public/datasets/lfw/index.html index 3f7dce60..d079c978 100644 --- a/site/public/datasets/lfw/index.html +++ b/site/public/datasets/lfw/index.html @@ -28,7 +28,7 @@

Labeled Faces in the Wild

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Created
2007
Images
13,233
People
5,749
Created From
Yahoo News images
Search available
Searchable
Eighteen of the 5,749 people in the Labeled Faces in the Wild Dataset. The most widely used face dataset for benchmarking commercial face recognition algorithms.
Eighteen of the 5,749 people in the Labeled Faces in the Wild Dataset. The most widely used face dataset for benchmarking commercial face recognition algorithms.

Intro

+
Created
2007
Images
13,233
People
5,749
Created From
Yahoo News images
Search available
Searchable
Eighteen of the 5,749 people in the Labeled Faces in the Wild Dataset. The most widely used face dataset for benchmarking commercial face recognition algorithms.
Eighteen of the 5,749 people in the Labeled Faces in the Wild Dataset. The most widely used face dataset for benchmarking commercial face recognition algorithms.

Intro

Labeled Faces in The Wild (LFW) is among the most widely used facial recognition training datasets in the world and is the first of its kind to be created entirely from images posted online. The LFW dataset includes 13,233 images of 5,749 people that were collected between 2002-2004. Use the tools below to check if you were included in this dataset or scroll down to read the analysis.

Three paragraphs describing the LFW dataset in a format that can be easily replicated for the other datasets. Nothing too custom. An analysis of the initial research papers with context relative to all the other dataset papers.

From George W. Bush to Jamie Lee Curtis: all 5,749 people in the LFW Dataset sorted from most to least images collected.
From George W. Bush to Jamie Lee Curtis: all 5,749 people in the LFW Dataset sorted from most to least images collected.

LFW by the Numbers

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According to BiometricUpdate.com [^lfw_pingan], LFW is "the most widely used evaluation set in the field of facial recognition, LFW attracts a few dozen teams from around the globe including Google, Facebook, Microsoft Research Asia, Baidu, Tencent, SenseTime, Face++ and Chinese University of Hong Kong."

According to researchers at the Baidu Research – Institute of Deep Learning "LFW has been the most popular evaluation benchmark for face recognition, and played a very important role in facilitating the face recognition society to improve algorithm. [^lfw_baidu]."

In addition to commercial use as an evaluation tool, alll of the faces in LFW dataset are prepackaged into a popular machine learning code framework called scikit-learn.

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@@ -102,17 +102,6 @@
Distribution of citations per year per country for the top 5 countries with citations for the LFW Dataset
Distribution of citations per year per country for the top 5 countries with citations for the LFW Dataset

Conclusion

The LFW face recognition training and evaluation dataset is a historically important face dataset as it was the first popular dataset to be created entirely from Internet images, paving the way for a global trend towards downloading anyone’s face from the Internet and adding it to a dataset. As will be evident with other datasets, LFW’s approach has now become the norm.

For all the 5,000 people in this datasets, their face is forever a part of facial recognition history. It would be impossible to remove anyone from the dataset because it is so ubiquitous. For their rest of the lives and forever after, these 5,000 people will continue to be used for training facial recognition surveillance.

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Right to Removal

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If you are affected by disclosure of your identity in this dataset please do contact the authors. Many have stated that they are willing to remove images upon request. The authors of the LFW dataset provide the following email for inquiries:

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You can use the following message to request removal from the dataset:

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To: Gary Huang mailto:gbhuang@cs.umass.edu

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Subject: Request for Removal from LFW Face Dataset

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Dear [researcher name],

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I am writing to you about the "Labeled Faces in The Wild Dataset". Recently I discovered that your dataset includes my identity and I no longer wish to be included in your dataset.

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The dataset is being used thousands of companies around the world to improve facial recognition software including usage by governments for the purpose of law enforcement, national security, tracking consumers in retail environments, and tracking individuals through public spaces.

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My name as it appears in your dataset is [your name]. Please remove all images from your dataset and inform your newsletter subscribers to likewise update their copies.

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- [your name]

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Supplementary Data

Researchers, journ

Company Country
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