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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.
1
Compared with human memory, machines can ________.
A.identify human faces more efficiently
B.tell a friend from a mere acquaintance
C.store an unlimited number of human faces
D.perceive images invisible to the human eye
本题答案:
  • A
  • B
  • C
  • D
  • 参考答案:A
  • 系统解析:
    由题干中的关键词human memory和machines定位到文章前三段。推理判断题。文章前两段指出人类的记忆是十分有限的,一个人最多也就能记住千余张面孔。第三段第一句指出,机器在此方面是没有限制的,第二句更是明确提到,只要给合适的电脑输入一个庞大的人脸数据库,它就可以以出色的速度和精确度识别人脸,可见机器在人脸识别方面比人类更有效,故答案为A。
2
Why did researchers create MegaFace?
A.To enlarge the volume of the facial-recognition database.
B.To increase the variety of facial-recognition software.
C.To understand computers' problems with facial recognition.
D.To reduce the complexity of facial-recognition algorithms.
本题答案:
  • A
  • B
  • C
  • D
  • 参考答案:C
  • 系统解析:
    由题干中的关键词MegaFace定位到文章第四段第三句。细节辨认题。由定位句可知,华盛顿大学的研究人员创建大型人脸数据库“百万面孔”的目的是为了弄清楚电脑在人脸识别方面所面临的困难,这与选项C的表述一致,故答案为C。
3
What does the passage say about machine accuracy?
A.It falls short of researchers' expectations.
B.It improves with added computing power.
C.It varies greatly with different algorithms.
D.It decreases as the database size increases.
本题答案:
  • A
  • B
  • C
  • D
  • 参考答案:D
  • 系统解析:
    由题干中的关键词machine accuracy定位到文章第五段第一句。细节辨认题。定位句指出,随着数据库的增大,机器的精确度全面下降,可见这与选项D的表述相同,故答案为D。
4
What is said to be a shortcoming-of facial-recognition machines?
A.They cannot easily tell apart people with near-identical appearances.
B.They have difficulty identifying changes in facial expressions.
C.They are not sensitive to minute changes in people's mood.
D.They have problems distinguishing people of the same age.
本题答案:
  • A
  • B
  • C
  • D
  • 参考答案:A
  • 系统解析:
    由题干中的关键词a shortcoming of facial-recognition machines定位到文章第六段。推理判断题。定位句前半部分指出,机器在处理相似度较高的人脸时也有困难,难以将长相极相似的人分辨为两个不同的人,可见A)的表述符合原文,故答案为A。
5
What is the difficulty confronting researchers of facial-recognition machines?
A.No computer is yet able to handle huge datasets of human faces.
B.There do not exist public databases with sufficient face samples.
C.There are no appropriate algorithms to process the face samples.
D.They have trouble converting face datasets into the right format.
本题答案:
  • A
  • B
  • C
  • D
  • 参考答案:B
  • 系统解析:
    由题干中的关键词the difficulty confronting researchers定位到文章倒数第二段前三句。细节辨认题。定位句指出,研究人员的问题在于他们没有现成的大规模数据库可用,一些大规模数据库都不是公共的,可见研究人员的困难就是他们没有充足的数据样本,故答案为B。
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