Say, do I know you?
Written on the A1M and dispatched to silicon.com via a free LAN connection provided by my hotel outside Newcastle upon Tyne
Have you noticed how easy it is to recognise a friend or loved one at a distance? How do we do that?
For sure it isn't on the basis of a single parameter recognition system. We don't look at the face alone, which may be indistinguishable or even turned away from us. We take a multi-parameter approach. Body size and shape, skin colour and tone, facial expressions, clothing, jewellery, mannerisms, gait, behaviour patterns all play a vital part in the complete picture that tells us, yep, that is my wife or husband, brother, sister or friend.
The efficacy of our biological (and multi-parameter) recognition systems is of course axiomatic as we use them, and live or die by them, every day. Strange then that we seem to persist in trying to create singular, or very limited, parameter systems in the ICT domain.
What gives? Why should we think that a hand or thumb, lip or ear, face or body, voice or other biometric print will suffice as part of a complete electronic recognition system? Beats me!
Hardly a day goes by without some champion of one system or another claiming they have found the holy grail of personal ID and security. But I believe all single parameter security systems are doomed to fail. So many humans look sufficiently alike that face and body recognition is bound to be poor, as is clothing and mannerisms taken singularly. The voice and other biometric data is easy to mimic or forge, and just about everything in isolation can be assumed to provide weak identification.
The answer is obvious - and well tried and tested over thousands of years. Just afford our ICT systems the luxury of a multi-parameter approach and recognition accuracy will improve dramatically. Let me illustrate with some incomplete but representative numbers as follows:
|Item||Recognition parameter||Nominal recognition error||Difficulty of implementation (1=easy, 10=hard)||Difficulty of use for subject (1=easy, 10=hard)||Nominal sensor and software cost|
|1||Body size||10 per cent||1||1||$300|
|2||Body shape||10 per cent||2||1||$300|
|3||Skin colour||5 per cent||4||1||$300|
|4||Clothing||5 per cent||3||1||$300|
|5||Mannerisms||5 per cent||4||1||$500|
|6||Gait||1 per cent||4||1||$500|
|7||Face||0.10 per cent||6||1||$300|
|8||Voice||0.10 per cent||10||3||$100|
|9||Hand||0.01 per cent||7||3||$300|
|10||Thumb||0.001 per cent||8||4||$50|
At this point it is interesting to reflect that the concatenation of items 1 to 10 provides a far greater degree of recognition accuracy than a genetic sample and a marginally better accuracy than an iris scan but is considerably less expensive than either the iris scan or a genetic sample. Moreover, the first 10 items are generally more convenient and easier to use and items 1 to 8 can be realised overtly or covertly.
So the concatenation of the simple overcomes the dedication of the complex!