文章大意:这是一篇说明文。人类的记忆是不可靠的。即使是面部识别能力最强的人也只能记住这么多,很难量化一个人的记忆力有多好。机器不受这种方式的限制。给正确的计算机一个巨大的人脸数据库,它就能以惊人的速度和精度处理它看到的东西——然后识别它被告知要找到的面孔。但机器在面部识别方面仍然有局限性,随着数据库的增长,机器的精确度全面下降。
7 .
Directions: After reading the passage below, fill in the blanks to make the passage coherent and grammatically correct. For the blanks with a given word, fill in each blank with the proper form of the given word; for the other blanks, use one word that best fits each blank.Human memory is notoriously (众所周知地) unreliable. Even people with the sharpest facial recognition skills can only remember so much.
It’s tough to quantify how good a person is 1 remembering. No one really knows how many different faces someone can recall, for example, but various estimates tend to hover in the thousands – based on the number of acquaintances a person 2 have.
Machines aren’t limited this way. Give the right computer a massive database of faces, and it can process what it sees – then recognize a face it 3 (tell) to find – with remarkable speed and precision. This skill is 4 supports the enormous promise of facial-recognition software in the 21st century. It is also what makes contemporary surveillance (监控) systems so scary.
The thing is, machines still have limitations when it comes to facial recognition. And scientists are only just beginning to understand what those constraints are. 5 (figure) out how computers are struggling, researchers at the University of Washington created a massive database of faces – they call it MegaFace – and 6 (test) a variety of facial-recognition algorithms(算法) as they scales up in complexity. The idea was to test the machines on a database that included up to 1 million different images of nearly 7,000 different people – and not just a large database 7 (feature) a relatively small number of different faces, more consistent with what’s been used in other research.
As the databases grew, machine accuracy dipped across the board. Algorithms 8 were right 95% of the time when they were dealing with a 13,000-image database, for example, were accurate about 70% of the time when 9 (face) with 1 million images. That’s still pretty good, says one of the researchers, Ira Kemelmacher-Shlizerman. “Much better than we expected,” she said,
Machines also had difficulty adjusting for people who look a lot alike –either doppelgangers (长相极相似的人), whom the machine would have trouble 10 (identify) as two separate people, or the same person who appeared in different photos at different ages or in different lighting, whom the machine would incorrectly view as separate people.