Reshaping the Way We Interact: AI Facial Recognition Technology
Imagine yourself as a detective working on a high-profile criminal case. You have a rough idea of what the suspect may look like but need additional evidence to take action. You have a new lead come in- a security camera with an image of the suspect near the crime scene. Through the use of facial recognition technology, you’re able to analyze the image and put a name to the face, helping solve the case. Believe it or not, this cutting-edge technology is also prevalent in daily life as it can serve to unlock an iPhone, make a payment, or take an image with your favorite Snapchat filter.
What is facial recognition technology?
Facial recognition technology is essentially a type of software technology capable of matching human faces and features to digital images. It’s used as a way to identify identities for the sake of security and authentication.
How does facial recognition technology work?
Through analyzing facial features from images, facial recognition technology can utilize various algorithms and patterns to analyze and compare facial patterns, creating a source of automated identification. Here’s a simple overview walking through its process:
It first detects a face in an image or video, which can be done through algorithms helping to analyze patterns and features.
It then aligns facial features based on position, scale, or rotation to provide an accurate comparison between other images and faces.
Next it extracts key facial features to create a numerical representation which can be done with machine learning algorithms, geometric measurements, or texture analysis.
Then the extracted face template is then compared to a database containing other images or templates of known faces. The algorithms look for similarities between the images during this step.
It then makes a determination on the given image, either matching it with another similar image or categorizing it into groups of gender, age, etc.
Finally it sets a decision threshold to determine the accuracy and validity of the image in its chosen categorization. It does so by balancing between false positives (when it incorrectly identifies a face) and false negatives (when it fails to identify a face that should match).
* It’s important to keep in mind that this isn’t the case for all facial recognition technology, and that all of them differ with their processes.
Types of algorithms that facial recognition technology could use in terms of machine learning include:
k-Nearest Neighbors (KNN): KNN is used when matching or identifying images or videos. Based on distance metrics, the algorithm is used to find the k most similar faces which serve as the “nearest neighbors” and assign an identity to the given input face based on them. Keep in mind that the k value indicates how many neighbors are considered.
Euclidean Distance: This is used as a distance metric to measure the similarity between vectors, normally representing the shortest distance between two. When extracting facial features from an image or video, Euclidean Distance is used to measure the distance between them and compares the results to other potential matches to find one that’s the most similar.
There are many more algorithms to cover, but if I went too in depth we’d be here forever!
What are specific use cases for facial recognition technology?
I have already listed some throughout the blog post, but to go more in detail, here’s a list of some more instances:
Can be used to gain access to buildings and facilities by recognizing faces
Helps to identify suspects from images or videos at crime scenes
Can be used to personalize an experience. Ex. Hotels can use facial recognition to greet you by name and retailers can offer personalized recommendations based on purchase history.
Can be used to identify individuals during emergency situations when some go missing or need aid from disasters.
Can be used to verify identities when traveling to detect illegal trespassing and improve security altogether.
Can be used to track attendance in areas to eliminate the need for manual tracking.
What are the potential limitations of using facial recognition technology?
There are a few limitations that facial recognition technology has that should be considered when using it.
Its accuracy is limited when it’s made to scan faces in conditions with poor lighting or variations in facial expressions. Different occlusions can affect it as it may not be used to it with its given datasets. This includes any glasses, hats, facial hair, piercings, etc.
There’s also bias surrounding its functioning as previous performance has shown higher error rates for women, children, and people with darker skin tones. It’s important to train such systems with data that’s diverse to prevent an underrepresentation of certain demographics.
There are several privacy concerns that emerge through its use as well, since it captures and stores an individual's biometric data. This could lead to potential misuse and unauthorized access with breaches or violations to the system.
With facial recognition technology becoming more accessible and widespread, more and more companies are utilizing it for their security. However, most times individuals are unaware when they’re being recorded and identified, which poses a serious issue as it raises questions about consent.
There could be potential misidentification through its use if it ever produces errors. This could result in wrongful accusations or a failure to identify the correct individuals.
There could be misuse with the technology as it could fall into the wrong hands and individuals could be in danger in terms of their own privacy and security. Such instances include stalking, identity theft, or social engineering.
Where can I learn more about facial recognition technology?
I’ve provided a list of valuable resources if you’re interested in learning more about the topic:
Academic Research Papers: All over the internet there are published journals or webinars that you can join coverints the topics of machine learning, pattern recognition, or computer vision. To find the most credible sources you can look at sources on google scholar or databases on MackinVIA.
Online Courses: There are numerous online learning platforms out there such as Udemy or edX that offer better, more in-depth understandings of machine learning and facial recognition technology. However, the one that was the most effective for me was the InspritAI scholars program as I was taught by various Stanford and Ivy graduates and got to work on a project covering facial recognition + AI with them!
Blogs and Websites: You’re already going in the right direction if you’re reading this blog! But there are a few other sources out there that’ll help to keep you up to date with ongoing trends and innovations. This includes OpenAI’s blog, Medium Blogs, or any AI-technology websites you may find on the internet.
Facial recognition technology is a powerful tool capable of providing countless benefits, from security and protection, to convenience and personalized experiences. But while the topic itself may be really neat, it's important to approach it with a critical mindset, considering both the technical and ethical aspects of it. As we continue to develop and refine these facial recognition systems, we need to prioritize fairness and accountability. Facial recognition technology needs to find that balance between innovating and safeguarding human rights. Ways that we could approach this could be through promoting more open dialogue and collaboration to ultimately strive for a future where facial recognition technology benefits society while respecting individuals privacy.
About Inspirit AI
AI Scholars Live Online is a 10 session (25-hour) program that exposes high school students to fundamental AI concepts and guides them to build a socially impactful project. Taught by our team of graduate students from Stanford, MIT, and more, students receive a personalized learning experience in small groups with a student-teacher ratio of 5:1.
