Future of Facial Recognition

Author: John Honovich, Published on May 04, 2013

The Boston Bombings reaffirmed how far facial recognition needs to develop to be an effective surveillance tool. Despite the PR spin jobs, facial recognition failed to make any difference. Proponents, of course, are quick to talk about how fast improvements are coming and how all this will change in the future (ironically, just like they said 5 years ago). However, one Carnegie Mellon professor had insightful comments about the issues and needs for facial recognition. In this note, we break them down and analyze their likelihood of development.

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Comments (5)

I don't see facial recognition being very succesful until the equivalent of a fingerprint is identified and incorporated into FR systems. Present day values such as the spacing of various parts of the face are insufficent for this purpose as they require both the subject and the comparison database images to be taken at approximately the same angle, elevation and distance to allow reasonably accurate comparisons.

For the technology to function accurately, some other means than standard 2D images will either have to supplant or enhance the means of identification. This could be heat patterns, under-skin structure (arteries, veins and cappliaries) iris prints, pore locations or some other more unique and less concealable criteria.

I worked on NEC's NeoFace Facial Recognition Software for 5 months, and it is worth to give a chance that software.

Can you expand on your use of it? Anything specific that they did that stood out? How was it used?

Does facial recognition software exploit the 3D information inherent in video?

As a person passes through a surveillance field of view, both horizontal and vertical aspects change significantly, providing the raw information necessary to construct a 3D model.

There is a well developed body of literature that addresses mechanisms to expand multi aspect observations into a 3D model of elements of interest within the scene.

The literature also addresses mechanisms to integrate deformable maps into a consistent 3D model (facial expressions may change, persons may be speaking, etc. which increases the challenge of extrapolating from multiple frames into a ocnsistent 3D model).

A more complex 3D model could increase points of difference across similar populations as well as reduce aspect dependence.

Another complication the article did not address is the fact that people's faces are asymetrical, so for example a left view may differ substantially from a right view. Of course, one cannot accurately reconstruct elements not present within the observed scene, again limiting the dimensionality of the match.

This article is a little old, but Facial Recognition stills as a interessant technology to many applications.

What about 2018... does anyone have a up-to-date opinion? What can we expect about the present point of evolution?

I have heard about GPU processing, 3D face recognition, rotation compensation, etc... How this technologies can improve the accurance and what is the impact in infraestructure?

Recently I saw a article from Ayonix launching an Axis's cameras embedded 3D facial recognition. They claim that their solution has a 99.X hit-rate using a single server from 10.000 cameras... Did anyone test this?


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