Expert author Ravi Das has, in previous articles, described DNA recognition and Gait recognition as physical biometric technologies of the future. Here, he presents earlobe recognition as another technology that may be utilized in the future.
Studies of the unique features of earlobe recognition date back to the 19th century. In fact, an ear classification system was first proposed in 1964 by scientist Alfred Iannerreli. In the mid 2000’s, the European Commission further studied the use of the ear as a potential biometric.
An example of the outer ear
Earlobe recognition is a potential physical biometric, in which the unique features are extracted and examined. The ear is fully shaped by the time an infant turns four months of age. As a potential biometric, the structure of the outer ear is of prime interest.
Earlobe Recognition: How It Works
Current research is focused upon using a technique known as “Eigenears”, which is based upon the different structures of the outer ear. Eigenears is very similar to the technique of “Eigenfaces” used in facial recognition. Thus far, there are positive results in the scientific community that the outer ear is unique in its overall shape, in terms of depth, angular structure, and the overall formation of the earlobe structure.
Scientists are currently examining the distances between the regions of the ear, in a forty-five degree and 3-dimensional angular models. The raw data of the ear can be captured by taking a picture of the outer ear, preferably when the ear is placed against a glass platen in order to capture raw images. These raw images are then used for unique feature extraction and biometric template creation. Either a 3-dimensional or a thermal picture can be taken of the outer ear, with preference being given to the latter.
At the present time, there are two methodologies being examined for potential earlobe recognition. They are:
- Principal Component Analysis: With this technique, the raw image of the ear is cropped, then scaled to a regular size, thus capturing the two keypoints of the ear, specifically the regions known as the triangular fossa and the antiragus, from which the unique features can be extracted.
- 3-Dimensional Analysis: With this technique, the image of the outer ear is captured, thus ascertaining both the depth and the color type of the ear. Both the helix and the antihelix are examined, from which the unique features can be captured.
Earlobe Recognition: Advantages & Disadvantages
Also, the potential of earlobe recognition can also be examined from the same seven criterion that all of the other biometric technologies have been analyzed against:
- Universality: For the most part, unless an individual has a physical injury which caused them to have an absence of the ear, most people have an ear which can be examined for unique features.
- Uniqueness: Although it is still not yet proven scientifically, preliminary results have shown that the two tests involving 10,000 earlobes and identical twins, all individuals were to have found to possess a unique ear structure.
- Permanence: The shape of the ear more or less stays the same over the lifetime of an individual.
- Collectability: Certain variables from the external environment can certainly impact the capture of the raw images of the ear, and these variables include different types and kinds of lighting situations, jewelry such as earrings, and wireless device accessories which can be placed in the outer ear.
- Performance: It is still too early to determine how well earlobe recognition will actually be adopted and used in the commercial market.
- Acceptability: Earlobe recognition is deemed to be a potentially non-intrusive type of potential biometric technology, thus there should be no issues with regards to privacy rights.
- Resistance to circumvention: At this point, it is difficult to determine but it is possible to construct a false earlobe.
Currently, there are no commercial applications of earlobe recognition. However, it is highly anticipated that it will work well with facial recognition systems in the future, in multimodal biometric solution applications.
Conclusions: DNA Recognition, Gait Recognition, and Earlobe Recognition
Of the three potential biometric technologies covered in the past articles, gait recognition appears to offer most potential in terms of practical deployment. Its potential is very strong for multi-modal security applications. Think, for example, of a dual-level verification system whereby a subject’s identity is initially established on the basis of, for example, a fingerprint before being verified using gait recognition. It is also useful for one-to-many verification requirements at, for example, airports.
Under these conditions, gait recognition can offer significant time savings by allowing large groups of people to be identified in a single environment. Compared to gait recognition, earlobe recognition has much further to go, starting with research and development. This also applies to DNA recognition, even though it has the potential to be the best biometric of the three we have covered, due to its uniqueness.
Ravi Das is a Cybersecurity Consultant and Business Development Specialist. He also does Cybersecurity Consulting through his private practice, RaviDas Tech, Inc. He also possesses the Certified in Cybersecurity (CC) cert from the ISC2.
Visit his website at mltechnologies.io
