That would be false detection rate, and a 98% FDR is usually somewhat better than 98% false positive rate. To avoid writing it all out again, wikipedia lists the possible permutations: https://en.wikipedia.org/wiki/Sensitivity_and_specificity
98% false positive rate is awful, 98% false detection rate may be acceptable depending on application. False positive rate (or alternatively, specificity) and sensitivity are the easy parameters to measure if you've got a ground truth, all the others: false detection rate, accuracy, etc. depend on the proportion of true positives and negatives in the sample you're applying them too, and when looking for needles in haystacks they can produce very different numbers. I'm not sure what they were actually doing, applying this to crowds from CCTV and pulling in possible matches (in which case actual false positives would seem quite low if there were only 35 matches) or taking snaps of people they stopped anyway and running them through the search (in which case you need to know the number checked this was).
And all the above is based on yes/no classification. If you've got multiple possible classifications then as others have mentioned your false positives for one match might also be false negatives for others.