Facial recognition has been around for years, but its usage has become more practical in the past few years. It now powers innovative solutions, such as personal photo applications and secondary authentication for mobile devices. To understand these emerging capabilities, let's first discuss how facial recognition works.
Facial recognition allows the user to understand the attributes or features. For example, we can recognize attributes such as visual geometry of a face, mood, color as well as eyes. These attributes are beneficial to organize or search through millions of images in seconds. It uses metadata tags to search through millions of images or to identify a person.
How does facial recognition work?
Every day you're exposed to dozens of faces- some familiar, some not. Yet, with a glance, your brain assesses the face attributes and fits them to the corresponding individual. That's precisely how a facial recognition system works. Facial recognition technology sees data on a grand algorithmic scale.
So how does facial recognition work? Technologies vary, but here are the necessary steps:
- Capture: The First step is to collect physical or behavioral samples in predetermined conditions. Faces are matched based on their visual geometry, including the relationship between the eyes, nose, brow, mouth, and other facial features.
- Extraction: When images are analyzed for critical factors include that include the distance between your eyes and the distance from forehead to chin, the software identifies facial landmarks and creates a signature.
- Comparison: When customers are running a face search, the software is comparing this data from the source image to each of the pictures it searches.
- Matching: A likelihood that one face is a potential match for another is established. The final stage of face detection technology is to make a decision whether the face's features of a new sample are matching with the one from a facial database or not. It usually takes just seconds.
Some use cases of Facial recognition used by customers
An analytics company, for example, uses artificial intelligence to provide agencies with tools that assist them in identifying and locating victims of human trafficking. Investigators search automatically through millions of records in seconds. It saves a lot of time which previously required individual analysis by an investigator.
Another example is a financial services company. Using Facial recognition to detect and compare faces, this company can provide identity verification, without any human intervention. This use-case of facial recognition allows for more individuals to receive access to banking services than was ever previously possible.
In the retail sector, heat- mapping, for instance, is an excellent method through which crowd detection can be done to strategize the products and promotions better. Popular and unpopular product categories can be identified, and the sales strategy can be crafted accordingly.
Facial recognition is useful across many applications and industry verticals. In the few examples illustrated above of the different sectors, every industry aims to enhance its line of productivity, which can be achieved with facial recognition. Facial recognition technology will aid in the effective elimination of problems and help increase efficiency when it is enforced with the right intent and moral compass.
Limitations of Facial recognition
- Poor image quality limits facial recognition's effectiveness: A notable disadvantage of the facial recognition system is that it is less reliable and efficient than other biometric systems such as a fingerprint. Factors such as illumination, expression, and image or video quality, as well as software and hardware capabilities, can affect the performance of the system.
- Different face angles can throw off facial recognition's reliability: Several reports have pointed out the ineffectiveness of some systems. Anything less than a frontal view affects the algorithm's capability to generate a template for the face. The more direct the image (both enrolled and probe image) and the higher its resolution, the higher the score of any resulting matches.
- Concerns about racial bias and privacy laws: A study by the American Civil Liberties Union revealed that the Rekognition technology developed by Amazon failed nearly 40 percent false matches in tests involving people of color. In general, the system has been criticized for perpetuating privacy rights violations.
- Data processing and storage can limit facial recognition technology: Professional agencies use whole clusters of computers to minimize total processing time. But every added equipment means considerable data transfer via network, which can be influenced by input-output limitations that lower a processing speed.
Conclusion
Naturally, no technology is completely risk-free. Facial recognition is data-intense, which can render processing and storage a big hindrance. Despite enormous developments, the recognition of faces from various camera angles or with obstructions (such as hairstyles) is still not perfect. In addition, there have been disputes over privacy issues, especially in retail and government environments.
Higher-definition cameras will become accessible as technology improves. More information can be moved by computer networks, and processors will operate quicker. Algorithms for facial recognition will be better prepared to select faces from a picture and acknowledge them in a registered individual database. It will be easy to overcome the easy processes that defeat today's algorithms, such as darkening sections of the face with sunglasses and masks or altering one's hairstyle.
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