Headline May 25, 2019/ '' 'HALF FACE HUNT' '' : STUDENTS


THEORY OF MIND : They worked with a dataset containing multiple photos - 2800 in total - Yes, 2800 in total-

Of 200 students and staff from FEI University in Brazil, with equal numbers of men and women.

For the First Experiment - the team trained the model using only full facial images. So a word on  Machine Learning, first.

Machine Learning [ML]
It is a method where the target [goal] is defined and the steps to reach that target is learned by the machine itself training [gaining experience]. For example to identify a simple subject such as apple or an orange.

The target is achieved not by explicitly specifying the details about it and coding it but it is just as we reach a child by showing multiple different pictures of it and therefore allowing the machine to define the steps to identify it like an apple or an orange.

FACIAL RECOGNITION TECHNOLOGY works even when only half a face is visible, researchers from the University of Bradford have found.

Using artificial intelligence teams, the team achieved 100 percent recognition rates for both three-quarter and half faces.

The study published in Future Generation Computer Systems, is the first to use machine learning to test the recognition rates for different parts of the face.

Lead researcher, Professor  Hassan Ugail from the University of Bradford said :

''The ability humans have to recognize faces is amazing., but research has shown it starts to falter when we can only see parts of a face.

Computers can already perform better than humans in recognizing one face from a large number, so we wanted to see if they would be better at partial recognition as well.

The team used a machine learning technique known as a ''convolutional neural network,'' drawing on a feature extraction model called VGG - one of the most popular and widely used for facial recognition.

They worked with a dataset containing multiple photos - 2800 in total - of 200 students and staff from FEI University in Brazil, with equal numbers of men and women.

For the first experiment, the team trained the model using only facial images.

They then ran an experiment to see how well the computer was able to recognise faces, even when shown only part of them.

The computer recognised full faces 100 percent of the time, but the team also had 100% success with three-quarters faces and with the top or right half of the face.

However, the bottom half of the face was only correctly recognised 60 percent of the time and eyes and nose on their own, just 40 percent.

They then ran the experiment again, after training the model using partial facial images as well.

This time, the scores significantly improved for the bottom half of the face, for eyes and nose on their own and even for faces with no eyes and nose visible, achieving around 90% correct identification.

Individual facial parts, such as the nose, cheek forehead or mouth had low recognition rates in both experiments.

The results are promising, according to Professor Hassan:

''We've now shown that it's possible to have very accurate facial recognition from images that only show a part of face and we've identified which parts are most useful.

This opens up greater possibilities for the use of the technology for security or crime prevention.

''Our experiments now need validating on a much larger dataset. However, in the future it's likely that image databases used for facial recognition will need to include partial images as well -

So that the models can be trained correctly to recognize a face even when not all of it is visible.

The World Students Society thanks University of Bradford.

With respectful dedication to the Students, Professors and Teachers of the world.

See Ya all prepare for Great Global Elections and ''register'' on The World Students Society : wssciw.blogspot.com and Twitter-!E-WOW! - the ecosystem 2011:

''' Natural Language Processing '''

Good Night and God Bless

SAM Daily Times - the Voice of the Voiceless


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