Archive for the ‘Face Detection’ Category

3D Face Reconstruction from a Single Image

Monday, September 18th, 2017

3D Face Reconstruction from a Single Image by Aaron S. Jackson, Adrian Bulat, Vasileios Argyriou and Georgios Tzimiropoulos, Computer Vision Laboratory, The University of Nottingham.

From the webpage:

This is an online demo of our paper Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression. Take a look at our project website to read the paper and get the code. Please use a (close to) frontal image, or the face detector won’t see you (dlib)

Images and 3D reconstructions will be deleted within 20 minutes. They will not be used for anything other than this demo.

Very impressive!

You can upload your own image or use an example face.

Here’s an example I stole from Reza Zadeh:

This has all manner of interesting possibilities. 😉


PS: Torch7/MATLAB code for “Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression”

Deanonymizing the Past

Thursday, July 6th, 2017

What Ever Happened to All the Old Racist Whites from those Civil Rights Photos? by Johnny Silvercloud raises an interesting question but never considers it from a modern technology perspective.

Silvercloud includes this lunch counter image:

I count almost twenty (20) full or partial faces in this one image. Thousands if not hundreds of thousands of other images from the civil rights era capture similar scenes.

Then it occurred to me, unlike prior generations with volumes of photographs, populated by anonymous bystanders/perpetrators to/of infamous acts, we have the present capacity to deanonimize the past.

As a starting point, may I suggest Deep Face Recognition by Omkar M. Parkhi, Andrea Vedaldi, Andrew Zisserman, one of the more popular papers in this area, with 429 citations as of today (06 July 2017).


The goal of this paper is face recognition – from either a single photograph or from a set of faces tracked in a video. Recent progress in this area has been due to two factors: (i) end to end learning for the task using a convolutional neural network (CNN), and (ii) the availability of very large scale training datasets.

We make two contributions: first, we show how a very large scale dataset (2.6M images, over 2.6K people) can be assembled by a combination of automation and human in the loop, and discuss the trade off between data purity and time; second, we traverse through the complexities of deep network training and face recognition to present methods and procedures to achieve comparable state of the art results on the standard LFW and YTF face benchmarks.

That article was written in 2015 so consulting a 2017 summary update posted to Quora is advised for current details.

Banks, governments and others are using facial recognition for their own purposes, let’s also uses it to hold people responsible for their moral choices.

Moral choices at lunch counters, police riots, soldiers and camp guards from any number of countries and time periods, etc.


I’ll See You The FBI’s 411.9 million images and raise 300 million more, per day

Wednesday, June 15th, 2016

FBI Can Access Hundreds of Millions of Face Recognition Photos by Jennifer Lynch.

From the post:

Today the federal Government Accountability Office (GAO) finally published its exhaustive report on the FBI’s face recognition capabilities. The takeaway: FBI has access to hundreds of millions more photos than we ever thought. And the Bureau has been hiding this fact from the public—in flagrant violation of federal law and agency policy—for years.

According to the GAO Report, FBI’s Facial Analysis, Comparison, and Evaluation (FACE) Services unit not only has access to FBI’s Next Generation Identification (NGI) face recognition database of nearly 30 million civil and criminal mug shot photos, it also has access to the State Department’s Visa and Passport databases, the Defense Department’s biometric database, and the drivers license databases of at least 16 states. Totaling 411.9 million images, this is an unprecedented number of photographs, most of which are of Americans and foreigners who have committed no crimes.

I understand and share the concern over the FBI’s database of 411.9 million images from identification sources, but let’s be realistic about the FBI’s share of all the image data.

Not an exhaustive list but:

Facebook alone is equaling the FBI photo count every 1.3 days. Moreover, Facebook data is tied to both Facebook and very likely, other social media data, unlike my driver’s license.

Instagram takes a little over 5 days to exceed the FBI image count. but like the little engine that could, it keeps trying.

I’m not sure how to count YouTube’s 300 hours of video every minute.

No reliable counts are available for porn images, which streamed from Pornhub in 2015, accounted for 1,892 petabytes of data.

The Pornhub data stream includes a lot of duplication but finding non-religious and reliable stats on porn is difficult. Try searching for statistics on porn images. Speculation, guesses, etc.

Based on those figures, it’s fair to say the number of images available to the FBI is somewhere North of 100 billion and growing.

Oh, you think non-public photos off-limits to the FBI?

Hmmm, so is lying to federal judges, or so they say.

The FBI may say they are following safeguards, etc., but once a agency develops a culture of lying “in the public’s interest,” why would you ever believe them?

If you believe the FBI now, shouldn’t you say: Shame on me?

Facial Recognition Breakthrough!

Thursday, February 19th, 2015

A specialized face-processing network consistent with the representational geometry of monkey face patches by Amirhossein Farzmahdi, et al.


Ample evidence suggests that face processing in human and non-human primates is performed differently compared with other objects. Converging reports, both physiologically and psychophysically, indicate that faces are processed in specialized neural networks in the brain -i.e. face patches in monkeys and the fusiform face area (FFA) in humans. We are all expert face-processing agents, and able to identify very subtle differences within the category of faces, despite substantial visual and featural similarities. Identification is performed rapidly and accurately after viewing a whole face, while significantly drops if some of the face configurations (e.g. inversion, misalignment) are manipulated or if partial views of faces are shown due to occlusion. This refers to a hotly-debated, yet highly-supported concept, known as holistic face processing. We built a hierarchical computational model of face-processing based on evidence from recent neuronal and behavioural studies on faces processing in primates. Representational geometries of the last three layers of the model have characteristics similar to those observed in monkey face patches (posterior, middle and anterior patches). Furthermore, several face-processing-related phenomena reported in the literature automatically emerge as properties of this model. The representations are evolved through several computational layers, using biologically plausible learning rules. The model satisfies face inversion effect, composite face effect, other race effect, view and identity selectivity, and canonical face views. To our knowledge, no models have so far been proposed with this performance and agreement with biological data.

The article runs a full forty-eight (48) pages of citation laden text.

If you want a shorter synopsis, try: Human Face Recognition Found In Neural Network Based On Monkey Brains, which summarizes the paper and mentions the following similarities between human facial recognition and recognition by the neural network:

  • Both recognize faces easiest when seen between a full frontal and a profile
  • Both have difficulty recognizing faces when upside down
  • Composite faces, top and bottom from different people, are recognized by both as different people
  • If the neural network is trained on one race, has difficulty recognizing faces of other races, just like people

A large amount of investigation remains to be done, along with extending the methodology used here to explore and create the neural network.

From a privacy/security perspective, counter-measures will be needed to defeat ever more accurate facial recognition software.

50 Face Recognition APIs

Friday, October 24th, 2014

50 Face Recognition APIs by Mirko Krivanek.

Interesting listing published on Mashape. Only the top 12 are listed below. It would be nice to have a separate blog for voice recognition APIs. I’ve been thinking at using voice rather than passport or driving license, as a more secure ID. The voice has a texture unique to each individual.

Subjects that are likely to be of interest!

Mirko mentions voice but then lists face recognition APIs.

Voice comes up in a mixture of APIs in: 37 Recognition APIS: AT&T SPEECH, Moodstocks and Rekognition by Matthew Scott.

I first saw this in a tweet by Andrea Mostosi

BioID face database

Saturday, November 17th, 2012

BioID face database

From the webpage:

The BioID Face Database has been recorded and is published to give all researchers working in the area of face detection the possibility to compare the quality of their face detection algorithms with others. It may be used for such purposes without further permission. During the recording special emphasis has been placed on “real world” conditions. Therefore the testset features a large variety of illumination, background, and face size. Some typical sample images are shown below. (click to enlarge the images)

Just in case you are interested in face detection + topic maps.

I first saw this in Face detection using Python and OpenCV.

Face detection using Python and OpenCV

Saturday, November 17th, 2012

Face detection using Python and OpenCV by Paolo D’Incau.

From the post:

Most of the posts you will find in this blog are Erlang related (of course they are!), but sometimes I like writing also about my experiences at University of Trento as I am doing right now. During the last couple of years I have attended many courses about Computer Vision and Digital Signal Processing so today I would like to show you something about it.

In this post I will write about making some code for face detection purposes using python and OpenCV. This post will have no code, actually you can just grab my original code from here (the files needed are and haarcascade_frontalface_alt.xml).

Face detection is a computer technology that determines the locations and sizes of human faces in images or video. It detects facial features and ignores anything else, such as buildings, trees and bodies.

I can imagine any number of topic map applications that could use or be enhanced by face detection capabilities.