Biometrids — PRE-ICO open now! — Identity by facial recognition
A face recognition system is a computer application capable of identifying or verifying a person from a digital image or a video frame from a video source. One of the ways to do this is by comparing selected facial features from the image and a face database.
It is typically used in security systems and can be compared to other biometrics such as fingerprint or eye iris recognition systems. Recently, it has also become popular as a commercial identification and marketing tool.
Techniques for face acquisition
Some face recognition algorithms identify facial features by extracting landmarks, or features, from an image of the subject’s face. For example, an algorithm may analyze the relative position, size, and/or shape of the eyes, nose, cheekbones, and jaw. These features are then used to search for other images with matching features. Other algorithms normalize a gallery of face images and then compress the face data, only saving the data in the image that is useful for face recognition. A probe image is then compared with the face data. One of the earliest successful systems is based on template matching techniques applied to a set of salient facial features, providing a sort of compressed face representation.
Some face recognition algorithms identify facial features by extracting landmarks, or features, from an image of the subject’s face. For example, an algorithm may analyze the relative position, size, and/or shape of the eyes, nose, cheekbones, and jaw. These features are then used to search for other images with matching features. Other algorithms normalize a gallery of face images and then compress the face data, only saving the data in the image that is useful for face recognition. A probe image is then compared with the face data. One of the earliest successful systems is based on template matching techniques applied to a set of salient facial features, providing a sort of compressed face representation.
Recognition algorithms can be divided into two main approaches, geometric, which looks at distinguishing features, or photometric, which is a statistical approach that distills an image into values and compares the values with templates to eliminate variances.
Popular recognition algorithms include principal component analysis using eigenfaces, linear discriminant analysis, elastic bunch graph matching using the Fisherface algorithm, the hidden Markov model, the multilinear subspace learning using tensor representation, and the neuronal motivated dynamic link matching.
3-dimensional recognition
3-dimensional recognition
Three-dimensional face recognition technique uses 3D sensors to capture information about the shape of a face. This information is then used to identify distinctive features on the surface of a face, such as the contour of the eye sockets, nose, and chin.
One advantage of 3D face recognition is that it is not affected by changes in lighting like other techniques. It can also identify a face from a range of viewing angles, including a profile view. Three-dimensional data points from a face vastly improve the precision of face recognition. 3D research is enhanced by the development of sophisticated sensors that do a better job of capturing 3D face imagery. The sensors work by projecting structured light onto the face. Up to a dozen or more of these image sensors can be placed on the same CMOS chip — each sensor captures a different part of the spectrum….
Even a perfect 3D matching technique could be sensitive to expressions. For that goal a group at the Technion applied tools from metric geometry to treat expressions as isometries
A new method is to introduce a way to capture a 3D picture by using three tracking cameras that point at different angles; one camera will be pointing at the front of the subject, second one to the side, and third one at an angle. All these cameras will work together so it can track a subject’s face in real time and be able to face detect and recognize.
Skin texture analysis
Skin texture analysis
Another emerging trend uses the visual details of the skin, as captured in standard digital or scanned images. This technique, called skin texture analysis, turns the unique lines, patterns, and spots apparent in a person’s skin into a mathematical space.
Tests have shown that with the addition of skin texture analysis, performance in recognizing faces can increase 20 to 25 percent.
Thermal cameras
Thermal cameras
A different form of taking input data for face recognition is by using thermal cameras, by this procedure the cameras will only detect the shape of the head and it will ignore the subject accessories such as glasses, hats, or make up. A problem with using thermal pictures for face recognition is that the databases for face recognition is limited. Diego Socolinsky, and Andrea Selinger (2004) research the use of thermal face recognition in real life, and operation sceneries, and at the same time build a new database of thermal face images. The research uses low-sensitive, low-resolution ferro-electric electrics sensors that are capable of acquire long wave thermal infrared (LWIR). The results show that a fusion of LWIR and regular visual cameras has the greater results in outdoor probes. Indoor results show that visual has a 97.05% accuracy, while LWIR has 93.93%, and the Fusion has 98.40%, however on the outdoor proves visual has 67.06%, LWIR 83.03%, and fusion has 89.02%. The study used 240 subjects over the period of 10 weeks to create the new database. The data was collected on sunny, rainy, and cloudy days.
Biometrids
a new platform built to make it easier to identify the face of car device users. at once as a platform that can solve identity problems on blockchain
The Biometrids Platform lets people identify themselves with others using face recognition installed on their phones. Using a distributed ledger that does not change, everyone in the chain is unique. One face means one ID, and each ID is unique. If you are recorded in that chain once, you will never be able to manipulate that ID again or copy that ID. This will prevent identity theft and fraud, and will also ensure users are those they say.
The Biometrids Platform lets people identify themselves with others using face recognition installed on their phones. Using a distributed ledger that does not change, everyone in the chain is unique. One face means one ID, and each ID is unique. If you are recorded in that chain once, you will never be able to manipulate that ID again or copy that ID. This will prevent identity theft and fraud, and will also ensure users are those they say.
Token Sale
https://biometrids.io/
Name of the token will be IDS.
There will be a total of 100.000.000 IDS.
- 5% will be sold in pre-ICO.
- 5% will be for bounties and advisors.
- 70% will be sold doing the crowdsale.
- 10% will be for team.
- 10% will be for foundation.
Pre-ICO will run for one week and the price will be 910 IDS/1eth. The crowdsale will run for four weeks and prices will be:
- Week 1: 665 IDS/1eth
- Week 2: 550 IDS/1eth
- Week 3: 500 IDS/1eth
- Week 4: 450 IDS/1eth
Pre-Ico and crowdsale will run until end date, or until all coins are sold.
The 10% for team and the 10% for foundation will be locked up for three years.
Every unsold coin during the ICO will be locked for five years. After five years, they will be sold back to early investors in a private fundraising campaign. They will not be sold on exchanges.
ICO : https://biometrids.io/
TWITTER : h ttps://twitter.com/biometrids
BITCOINTALK : https://bitcointalk.org/index.php?action=profile
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