Risk of Violent Crime Victimization
Describe the authors' research questions, methodology, results, and findings.
Describe how the methodology helped the authors answer the research questions.
Describe how this article could or should influence public policy.
If you were to replicate the research of Lemieux and Felson using the data from Week 2 Project, would you have all the data you need to replicate their research? Are the Uniform Crime Report (UCR) statistics sufficient for reproducing their research?
What might be missing and preventing you from completing their research using the data from W2 Assignment 2?
How would you go about getting the needed information to complete their research?
Identify and describe other measures of crime in the United States that would be more appropriate to replicate their research and include a description of those data sources.
The Pros of the Automated Facial Recognition is that since the introduction of the 3Dimensional images finding people based on the images have become really authentic and exact compared to the earlier versions. It takes the image and rotates it till it finds the face of the inputted image.
Furthermore, Automated Facial Recognition compared to other forms of security systems like fingerprint scanners are quicker and much easier to use. Which means no one will have to worry about their identity being messed up by a burn on their finger or even a minor paper cut.
Facial recognition has a major benefit when it comes to security. Employees can enter their place of work using a facial recognition system for identification processes.
When used at work, it has another major benefit. No worker can sign in at work for another worker or as another worker, and no intruder will be able to access or trespass where they are not needed for example the manager’s office.
The Cons of Automated Recognition Software;
Every form of technology has its flaws which prevents it from working as desired.
The cons of a facial recognition system are that growing a facial hair can interfere with the recognition of a person. This can be simply resolved by getting the database updated periodically if they grow or shave their facial hair.
It can also mistake a person based on their weight change. The infrared cameras mostly do not mistake one individual for another but substantial weight changes can cause the software to misidentify individuals. However, like the facial hair problem we can solve this issue by periodically updating the software’s database.