In 1992, when most people had no idea what "biometrics" even meant, a paper published in the journalSensor Reviewdescribed a device that used blood vessel patterns in the palm for identity verification. It was called "Veincheck."
That same year, Windows 3.1 had just been released, and the internet was still a toy for a lucky few. This technology — using near‑infrared light to capture subcutaneous vein patterns for identification — seemed far too ahead of its time. Even a little strange.
More than three decades later, it‘s back. And this time, it might be right.
Let’s set aside the jargon for a moment.
Palm vein recognition does something very straightforward:you present your hand, it recognizes you.
But how does it work? It starts with a beam of light.
Hemoglobin in human blood strongly absorbs near‑infrared light at specific wavelengths — roughly between 760nm and 880nm. When a near‑infrared beam shines on your palm, the surrounding tissue reflects the light, but the veins absorb it. To the sensor, the veins appear as dark patterns against a lighter background.
That high‑contrast image is your vein “fingerprint.”
The key point: this fingerprint lies about3mm beneath your skin. You can‘t see it, you can’t touch it, and you can‘t copy it.
As of 2025, there are stillno publicly reported successful cases of palm vein forgery. Fingerprints can‘t claim that. Neither can faces.
This is the smartest part of palm vein technology.
Fingerprints can be molded into silicone. Faces can be bypassed with photos or videos. But palm vein recognition has a non‑negotiable requirement:blood must be flowing.
Only deoxygenated hemoglobin in moving blood effectively absorbs near‑infrared light. In other words, a detached hand — or a fake one — cannot pass the test.
This isn‘t an “add‑on” feature. It’s built into the physics of how the technology works.
Fujitsu, when marketing its PalmSecure line, emphasized exactly this point: the sensor only recognizes vein patterns that are “actively flowing within the individual‘s veins.” Any attempt to use a fake limb, a severed hand, or a molded replica is dead on arrival.
Palm vein recognition isn’t new. But for a long time, it was held back by one problem: feature extraction was too hard.
Traditional methods required hand‑crafted algorithms to extract vein patterns — heavily dependent on the researcher‘s experience and image quality. A 2014 academic paper put it bluntly: “high demand on image quality and high algorithm complexity.”
That changed with the widespread adoption of deep learning.
A 2019 thesis documented an experiment where researchers used a dual‑channel convolutional neural network (a type of deep learning model) for palm vein recognition. On the Hong Kong Polytechnic University’s public database (the PolyU database), it achieved an accuracy of99.90%. Traditional methods, on the same database, typically fluctuated between 95% and 98%.
The same paper tested another approach — transfer learning combined with random forest — and reached100%accuracy on the PolyU database.
Of course, academic database accuracy doesn‘t always translate to the real world. In practice, hand position, angle, and lighting all affect performance. A 2024 paper inIEEE Transactions on Information Forensics and Securityspecifically studied palm vein recognition under “unconstrained and weak‑cooperative conditions” — meaning: can the system still recognize a user who isn’t holding their hand perfectly still?
The consensus from these studies is consistent: algorithms are improving, hardware is getting cheaper, and palm vein recognition is moving from a “lab‑grade” technology to one that works reliably on a construction site.
Palm vein technology has disadvantages.
First: specialized hardware.Face recognition works with ordinary cameras. Palm vein requires dedicated infrared sensors and light sources. That means higher hardware costs and a higher barrier to entry.
Second: enrollment experience.Fingerprints take a single press. Faces take a quick glance. Palm vein requires the user to place their hand at a specific position and distance, and hold it there for a moment. First‑time users need a brief learning curve.
Third: environmental sensitivity.In extremely cold conditions, peripheral blood vessels constrict, and vein patterns can become fainter. Strong infrared interference — from certain industrial equipment or even natural infrared in sunlight — can also affect accuracy.
But these problems are being solved:
Technology matures gradually. Palm vein is at that stage where it’s moving from “usable” to “downright easy to use.”
Technology isn’t about being the most advanced. It‘s about being thebest fit for the scenario.
Scenario 1: Hot springs and water parks
Hands are wet. Phones are locked away. Faces are covered in steam. Fingerprints don’t work. Face recognition struggles. Wristbands are easy to lose. Palm vein is the only solution that requiresnothingfrom the user.
Scenario 2: Hospitals and laboratories
Doctors and researchers wear gloves. Fingerprint recognition is useless. Removing gloves to press a sensor is inconvenient and raises infection risks. Palm vein? Works even through gloves.
Scenario 3: Certain regions with facial covering customs
A 2025 industry report noted that in some parts of the Middle East, where women wear face veils, facial recognition is ineffective. Palm vein technology has become an important alternative for biometric solutions in those regions.
Scenario 4: High‑security payments
Credit cards can be cloned. Passwords can be observed. Faces can be spoofed with photos. Palm vein remains one of the most expensive biometric modalities to attack — with no successful public forgeries to date.
That‘s an interesting question.
The underlying principle of palm vein recognition was validated in the 1990s. Fujitsu began marketing its PalmSecure line in the early 2000s. Yet the technology never really broke out of niche markets.
Why?
First: cost.Sensors were expensive. Computing power was expensive. Terminals were expensive. In 2010, a palm vein access control system cost five to eight times more than a standard fingerprint system. Only bank vaults, data centers, and other ultra‑high‑security (and well‑funded) scenarios could afford it.
Second: strong alternatives.Fingerprint recognition exploded in the 2010s — cheap, easy, good enough for most applications. Face recognition, powered by deep learning, surged after 2015 and became the new mainstream. Palm vein sat in an awkward position: more expensive than fingerprint, slower than face.
Third: no ecosystem.Without enough terminals, there was no user habit. Without user habits, there were no more terminals. A classic “chicken‑and‑egg” problem.
But things have changed.
Hardware costs have dropped to an acceptable level. Post‑pandemic, demand for contactless interaction has surged. Data privacy concerns are making people rethink facial recognition. Palm vein — with its triple advantage ofcontactless + high security + privacy‑friendly— fits the moment perfectly.
A 2024 review article in the journalComputer Engineering and Applicationslisted palm vein recognition as “one of the current research hotspots in biometrics.” That‘s not marketing jargon. That’s an academic consensus.
I don‘t want to package this technology as a “perfect future black box.”
Palm vein recognition has limitations. It has a learning curve. In some scenarios, it’s not the best choice. But in others — wet hands, gloved hands, obscured faces, high‑security requirements — it offers a solution that no other technology can quite match.
Back in 1992, when that first Veincheck prototype was demonstrated, someone reportedly asked its inventor: “Why not just use fingerprints?”
The inventor‘s exact words are lost to time. But if I could speak for him, I think he might have said:
“Because the most secure things are often the ones you can’t see.”
