Limerick startup bought by Miami-based firm
Limerick startup EmotionReader has been acquired by facial recognition technology business Kairos.
Emotion Reader uses algorithms to analyse facial expressions in video content. Kairos is based in Miami and said it was a multimillion dollar deal.
The Limerick company was set up only last year by Dr Stephen Moore and Dr Padraig O'Leary.
Kairos wants to accelerate the adoption of facial recognition as a verification tool.
It said: "With EmotionReader's research team on board, Kairos will be working hard to push the limits of current face recognition systems to be more accurate in real world conditions. Specifically, in optimizing the algorithms to work without bias on all races, ethnicities, genders, and ages of faces."
Kairos said that "as face-recognition systems are adopted for new use cases, potential IP opportunities will be a focus to cement Kairos as a leader in this space". Dr Moore, Kairos's new chief scientific officer, said that "with recent advances in AI and deep learning we're at a tipping point where AI will change the lives of millions of people for the better."
Dr Moore had built up EmotionReader's research and development team working from a base in Singapore. Kairos said it would now consolidate its R&D team into the Singapore office, and that Dr Moore would lead the team.
Kairos CEO Brian Brackeen has previously written that facial recognition technology has problems with racial bias. He said there was an "absolute need to have honest dialogue around the troubling inefficiency of algorithms to properly identify women/people of colour".
"If you think of machine learning in terms of teaching a child, then consider that you cannot reasonably expect a child to recognise something or someone it has never or seldom seen. Similarly, in the case of algorithmic ethnic bias, the system can only be as diverse in its recognition of ethnicities as the catalogue of photos on which it has been trained."
He said algorithms should be offered "a much more diverse, expansive selection of images depicting dark-skinned women, various other shades of colour, and individuals identifying as "mixed" (which includes many ethnicities)".