CLOSE
Original image

Alphonse Bertillon and the Identity of Criminals

Original image

Alphonse Bertillon was a French forensic documentarian who developed or improved upon several methods of identifying criminals and solving crimes. Some of those methods, such as the mug shot, are still in use today, while others, particularly anthropometry, were abandoned over time in favor of more accurate methods. Bertillon is considered by many to be the first forensic expert.

Bertillon’s self-portrait as a mug shot.

Bertillon was a school dropout, and having been trained in no particular field other than that of a soldier, he went to work as a records clerk at the Prefecture of Police in Paris in 1879. The son and brother of statisticians, Bertillon was appalled at the chaos in the criminal offender files. In his spare time, he began to work out a better method. In France at the time, there was a concern over recidivists, or those who committed crimes over and over. Recidivists could draw harsher sentences, but they were difficult to identify, because arrestees were only identified by name and address, and sometimes a picture. But appearance and addresses change, and anyone could lie about their name. With the Paris criminal records system as it was in 1879, if you couldn’t ascertain a suspect’s name, you couldn’t find him in the files, and therefore the rate of recidivism was unknown. Suspected, but unknown.

Anthropometry

An illustration from a book on anthropometry by Alphonse Bertillon.

Bertillon tackled identifying criminals by anthropometry, or the measurements of man. Anthropometry has plenty of uses, in the fields of medicine, anthropology, and engineering, and Bertillon developed another: forensic anthropometry, for the purpose of identifying recidivists and keeping records of criminal offenders. His system, called bertillonage, involved measuring dimensions of the head, face, long bones of the limbs, and other body dimensions. Bertillion entered these measurements into file cards for each arrestee, and sorted them by the offender’s size. A suspected recidivist could be matched by these measurements, and then his name could be cross-referenced to his criminal record.

The major flaw in bertillonage was the assumption that measurements were different for each individual. Bertillon knew, from the Belgian statistician Lambert Quetelet, that the chances of two people being the same height were four to one. Bertillon surmised that the more measurements of different body parts he added, the longer the odds were that two people’s measurements would match. However, several of the measurements he included in his system were directly correlated with an individual’s height.

Still, Bertillion’s system identified recidivists better than any method used previously. In 1884 alone, 241 recidivists were identified when they were rearrested in Paris. The system spread throughout France, and then to other countries. An unsavory side effect was the idea that a “born criminal” could be identified by anthropometry before any crimes were committed, which fed into the eugenics debate.

Sir Francis Galton had his mug shot taken by Bertillon.

Bertillion’s anthropometry measurements were eventually replaced by the more accurate identifier of fingerprints, introduced into forensic science by Sir Francis Galton in the 1880s. But anthropometry wasn’t the only innovation Bertillon made in police record-keeping.

Mug Shots

Bertillon also had a system for incorporating face descriptions into criminal files, which he called “portrait parle.” This involved classifying the shapes of the eyes, nose, mouth, and other features into a coded lexicon that could be used as shorthand. However, the code was extensive and hard to teach to all the police in France, so portrait parle was abandoned in favor of mug shots.

François Bertillon, the photographer’s two-year-old son, mug shot taken in 1893.

Police had been using photography to record criminal appearance since shortly after photography was invented, but it was Alphonse Bertillon who standardized the mug shot into the familiar full-face shot accompanied by a profile view of the same size. The profile view was added because Bertillon saw that the unique shape of the ear is an identifier. His method, adopted in Paris in 1888, was soon used throughout France and in other countries.

Handwriting Analysis

The mug shot of Captain Alfred Dreyfus.

Bertillon’s brief foray into the science of handwriting analysis was a complete failure. He was called to testify in the Dreyfus Affair, in which Captain Alfred Dreyfus was accused of spying on the French military for Germany. The chief evidence against Dreyfus was a document, which he denied writing. There were no competent handwriting experts available, so the famous forensic expert Alphonse Bertillon was summoned, although he had no expertise in handwriting analysis. Bertillon’s initial examination of the document was inconclusive, but he eventually testified that the handwriting was Dreyfus’s, although allegedly Dreyfus had tried to disguise his handwriting as someone else imitating his handwriting. In other words, Bertillon said that Dreyfus was trying to frame someone of framing him. This convoluted logic is attributed to either Bertillon’s confidence that Dreyfus was guilty, or to the French military leaning on the police investigator to find Dreyfus guilty. Later analyses confirmed that Bertillon’s testimony on the handwriting was full of errors

Crime Scene Photography

Bertillon was also a proponent of crime scene photography. Photographing murder victims was important for capturing the ability to identify them before their bodies decayed or were disposed of. He developed a standardized technique of photographing a murder victim from above, in order to record the body’s position in situ before investigators disturbed the scene. Forensic measurements could be taken from the images any time afterward.

Although not all of Bertillon’s techniques panned out, he brought a sense of discipline to record keeping and crime investigation that opened doors for further developments in criminal justice.

This post was inspired by a picture found in an old issue of The Annals of Improbable Research, in which a cat is observing Bertillon at work.

Original image
iStock // Ekaterina Minaeva
technology
arrow
Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
May 21, 2017
Original image
iStock // Ekaterina Minaeva

Jacques Mattheij made a small, but awesome, mistake. He went on eBay one evening and bid on a bunch of bulk LEGO brick auctions, then went to sleep. Upon waking, he discovered that he was the high bidder on many, and was now the proud owner of two tons of LEGO bricks. (This is about 4400 pounds.) He wrote, "[L]esson 1: if you win almost all bids you are bidding too high."

Mattheij had noticed that bulk, unsorted bricks sell for something like €10/kilogram, whereas sets are roughly €40/kg and rare parts go for up to €100/kg. Much of the value of the bricks is in their sorting. If he could reduce the entropy of these bins of unsorted bricks, he could make a tidy profit. While many people do this work by hand, the problem is enormous—just the kind of challenge for a computer. Mattheij writes:

There are 38000+ shapes and there are 100+ possible shades of color (you can roughly tell how old someone is by asking them what lego colors they remember from their youth).

In the following months, Mattheij built a proof-of-concept sorting system using, of course, LEGO. He broke the problem down into a series of sub-problems (including "feeding LEGO reliably from a hopper is surprisingly hard," one of those facts of nature that will stymie even the best system design). After tinkering with the prototype at length, he expanded the system to a surprisingly complex system of conveyer belts (powered by a home treadmill), various pieces of cabinetry, and "copious quantities of crazy glue."

Here's a video showing the current system running at low speed:

The key part of the system was running the bricks past a camera paired with a computer running a neural net-based image classifier. That allows the computer (when sufficiently trained on brick images) to recognize bricks and thus categorize them by color, shape, or other parameters. Remember that as bricks pass by, they can be in any orientation, can be dirty, can even be stuck to other pieces. So having a flexible software system is key to recognizing—in a fraction of a second—what a given brick is, in order to sort it out. When a match is found, a jet of compressed air pops the piece off the conveyer belt and into a waiting bin.

After much experimentation, Mattheij rewrote the software (several times in fact) to accomplish a variety of basic tasks. At its core, the system takes images from a webcam and feeds them to a neural network to do the classification. Of course, the neural net needs to be "trained" by showing it lots of images, and telling it what those images represent. Mattheij's breakthrough was allowing the machine to effectively train itself, with guidance: Running pieces through allows the system to take its own photos, make a guess, and build on that guess. As long as Mattheij corrects the incorrect guesses, he ends up with a decent (and self-reinforcing) corpus of training data. As the machine continues running, it can rack up more training, allowing it to recognize a broad variety of pieces on the fly.

Here's another video, focusing on how the pieces move on conveyer belts (running at slow speed so puny humans can follow). You can also see the air jets in action:

In an email interview, Mattheij told Mental Floss that the system currently sorts LEGO bricks into more than 50 categories. It can also be run in a color-sorting mode to bin the parts across 12 color groups. (Thus at present you'd likely do a two-pass sort on the bricks: once for shape, then a separate pass for color.) He continues to refine the system, with a focus on making its recognition abilities faster. At some point down the line, he plans to make the software portion open source. You're on your own as far as building conveyer belts, bins, and so forth.

Check out Mattheij's writeup in two parts for more information. It starts with an overview of the story, followed up with a deep dive on the software. He's also tweeting about the project (among other things). And if you look around a bit, you'll find bulk LEGO brick auctions online—it's definitely a thing!

Original image
Nick Briggs/Comic Relief
entertainment
arrow
What Happened to Jamie and Aurelia From Love Actually?
May 26, 2017
Original image
Nick Briggs/Comic Relief

Fans of the romantic-comedy Love Actually recently got a bonus reunion in the form of Red Nose Day Actually, a short charity special that gave audiences a peek at where their favorite characters ended up almost 15 years later.

One of the most improbable pairings from the original film was between Jamie (Colin Firth) and Aurelia (Lúcia Moniz), who fell in love despite almost no shared vocabulary. Jamie is English, and Aurelia is Portuguese, and they know just enough of each other’s native tongues for Jamie to propose and Aurelia to accept.

A decade and a half on, they have both improved their knowledge of each other’s languages—if not perfectly, in Jamie’s case. But apparently, their love is much stronger than his grasp on Portuguese grammar, because they’ve got three bilingual kids and another on the way. (And still enjoy having important romantic moments in the car.)

In 2015, Love Actually script editor Emma Freud revealed via Twitter what happened between Karen and Harry (Emma Thompson and Alan Rickman, who passed away last year). Most of the other couples get happy endings in the short—even if Hugh Grant's character hasn't gotten any better at dancing.

[h/t TV Guide]

SECTIONS
BIG QUESTIONS
BIG QUESTIONS
WEATHER WATCH
BE THE CHANGE
JOB SECRETS
QUIZZES
WORLD WAR 1
SMART SHOPPING
STONES, BONES, & WRECKS
#TBT
THE PRESIDENTS
WORDS
RETROBITUARIES