Human memory is notoriously unreliable. Even people with the sharpest facial-recognition skills can only remember so much.

    It’s tough to quantify how good a person is at 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 might 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’s told to find—with remarkable speed and precision. This skill is what supports the enormous promise of facial-recognition software in the 21st century. It’s also what makes contemporary surveillance 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. To begin to figure out how computers are struggling, researchers at the University of Washington created a massive database of faces—they call it MegaFace—and tested a variety of facial recognition algorithms（算法） as they scaled up in complexity. The idea was to test the machines on a database that included up to 1 million different images of nearly 700,000 different people—and not just a large database featuring 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 that 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 confronted 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 identifying 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.

    “Once we scale up, algorithms must be sensitive to tiny changes in identities and at the same time invariant to lighting, pose, age,” Kemelmacher-Shlizerman said.

    The trouble is, for many of the researchers who’d like to design systems to address these challenges, massive datasets for experimentation just don’t exist—at least, not in formats that are accessible to academic researchers. Training sets like the ones Google and Facebook have are private. There are no public databases that contain millions of faces. MegaFace’s creators say it’s the largest publicly available facial-recognition dataset out there.

    “An ultimate face recognition algorithm should perform with billions of people in a dataset,” the researchers wrote.

50. B）There do not exist public databases with sufficient face samples.

解析：B。根据题干中的the difficulty confronting researchers可定位至文章倒数第二段第一句和第三句。根据原文可知，对于许多想要设计系统来解决这些挑战的研究人员来说，大量的实验数据集根本不存在。第三句提到，没有包含数百万张脸的公共数据库。故正确答案为B。原文中只提到了随着数据库的增长，机器的精度会随之下降，但并不说明计算机的没有这个能力处理庞大的人脸数据集，故排除A；原文中虽然提到了目前的算法在处理庞大的人脸数据集时会有局限，但没有说明找不到合适的算法来处理人脸样本，故排除C；原文定位句中提到，大量的实验数据集根本不存在，就算存在，研究人员也接触不到正确格式的数据库，并没有说明是研究人员自己对数据库格式进行转换，故排除D。