In today’s world, the need to maintain the security of information and physical property is becoming both increasingly crucial and challenging. The implementation of facial recognition systems is increasing to enhance and improve security and safety worldwide. Further, recent advancements extend its application from authentication to general business functions such as marketing and advertising.
The mounting number of applications and use cases substantiate facial recognition as one of the best alternatives to fingerprint, retina, and signature identification. As facial recognition technology represents one of the latest practical advancements in the automation of identity verification, these applications are a ready fit for advanced crime prevention, video surveillance, forensic investigations, transaction security, access control, and even for uses as diverse as diagnosing diseases.
A case for facial recognition
A facial recognition system is a complex image processing architecture that performs automated exhaustive matches of a human face from a digital image or video frame against an extensive database of facial images. The system must accurately assess the nuances of illumination, occlusion, and various imaging conditions that complicate the derivation of an accurate match from the available data sets.
Facial recognition technology is now one of the most demanded identification solutions. Whether it be an online identity verification or a real-time person detection via image, video, or audio-visual element, facial recognition is one of the most reliable tools to assess and identify person matches objectively. Unlike other identification solutions such as passwords, verification by email, or text message, biometric facial recognition uses advanced mathematical algorithms and dynamic patterns, making the system one of the most effective and accurate.
As the need for fast and accurate identification increases, so goes the level of research and new application advancements within the domain. According to a Markets and Markets report, the global facial recognition market is growing from USD 3.8 billion in 2020 to USD 8.5 billion by 2025. This article outlines the fundamental elements within a facial recognition system, including the associated data science models and challenges presented for model accuracy.
Deriving the model
As mentioned previously, facial recognition involves identifying a human face given a complex image processing architecture. In most cases, we can divide a face recognition algorithm into the following functional modules:
– A face image detector: identifies a human face’s location from a typical picture given both simple and complex backgrounds
– A face recognizer: a recognition model that identifies faces
System identification and data storage provide the foundation for face recognition analysis. Even though there are numerous facial recognition approaches, they typically follow the same basic framework.
1. Image input
For all facial recognition models, training and updating data sets with new image input is the foremost task. Thus, the first stage of facial recognition consists of deep learning model instructions and data inputs. In the first stage, the user provides the image input to identify while a camera or visual recognition tool constructs a facial blueprint for identification.
2. Face detection
Another stage of a facial recognition system detects the human face, whether alone or in a crowd. A face detection and extraction algorithm transforms the input image pixels into a vector representation for facial detection processing and identification. And finally, an algorithm establishes whether the image is a human face.
3. Face recognition
An advanced mathematical algorithm turns the facial blueprint into numerical code known as the face print during the face recognition stage. The face recognizer classifies the feature vector and identifies that the face is already registered in the database.
4. Person identity
In the prior stage, the recognizer identified whether the image matches with the data set or not. In the final step, facial recognition algorithms identify a human face match with the exact features. Note that every person has a unique face print.
Key challenges with a facial recognition model
Face recognition is one of the most challenging tasks to perform in image recognition. One reason for this is that a human face is a 3D object and a non-rigid body. And significantly, many of the images are taken in a natural environment with complex image backgrounds. Thus, factors influencing facial recognition model accuracy can include several variables such as
1. Complex backgrounds
2. Occlusion
3. Rotation
4. Illumination
5. Scaling
6. Distortion
7. Facial expressions
8. Makeup and hairstyle
Today, facial recognition applications may range anywhere from security and surveillance to marketing and advertising. These successful applications credit their success to data science deep learning models and artificial intelligence-powered software. For instance, intelligent digital billboards harness computer vision’s ability to serve up ads based on an estimate of a person’s sex, age, and mood. Law enforcement agencies utilize facial recognition to locate suspects or missing individuals. Businesses and government use facial recognition to control access to facilities, services, or events.
As the use and capabilities of computer vision-powered facial recognition continue to expand, more organizations derive significant benefit from improved security and elevated customer experiences. Facial recognition has set the bar for automated, precise, and instant identification.
If you would like to learn more about implementing facial recognition in your business, send us your query to intellect2@intellect2.ai. Intellect Data, Inc. is a software solutions company incorporating data science and artificial intelligence into modern digital products with Intellect 2 TM. Intellect DataTM develops and implements software, software components, and software as a service (SaaS) for enterprise, desktop, web, mobile, cloud, IoT, wearables, and AR/VR environments. Locate us on the web at www.intellect2.ai.