How image data collection is used for Facial Recognition?
Introduction
Imagine a world in which there is no need for ID cards, passports, or any physical identification at all. This world would be possible once we could recognize humans using facial recognition on our phones, computers and tablets. With the rise of social media and the capture of image data, it’s easier for facial recognition software to find your photos. Even if you’re not using a facial recognition app, there is a very good chance that your face has been captured in a photo on Facebook or Instagram at some point in time.
Facial recognition is nothing new, in fact, it's been here since the 1960s, but it sparked conversations after the 2010s when Facebook started recognizing people in images. It was used to unlock our smartphones from the beginning and recently moved to more serious issues in law enforcement. Imagine what it could do as the technology progresses! But in order to develop a model that recognizes faces, a large amount of image data collection is required. In this article, we will know what facial recognition is, how it works, its use cases, and more.
What is Facial Recognition?
Facial Recognition is a method of using your face to identify or confirm your identity. People can be identified in pictures, films, or in real-time using facial recognition technology. Facial recognition has traditionally functioned in the same way as other “biometric” identifying methods including voice recognition, eye irises, and fingerprint identification. For example, fingerprint data is collected and processed for identifying markers. A newly discovered fingerprint can then be compared to this database to see whether any markers match. In the same way, facial recognition works. A computer examines visual data and searches for a specified set of indicators, such as a person’s head shape and the depth of their eye sockets.
How do Facial Recognition works?
The facial recognition industry is rapidly growing, thanks to advances in AI, machine learning, and deep learning technologies. Facial recognition is a type of technology that can identify a person only by looking at them. It identifies, collects, stores, and evaluates facial traits so that they can be matched to images of people in a database using machine learning techniques. It’s difficult to say exactly how facial recognition works. However, in order to understand the idea, we must consider certain key issues that a machine must address in order to advance. Face detection, feature extraction, face recognition, and face verification procedures are the approaches used, and to make all this possible, we need high-quality and customized AI training datasets.
1. Detection: The system must first locate the face in the image or video. The majority of cameras now include a built-in facial recognition feature. Face recognition is used by Snapchat, Facebook, and other social media platforms to allow users to apply effects to photographs and video dataset recorded using their apps. Many apps use this face detection approach to identify the person in the photo, they can even pinpoint a person standing in a crowd.
2. Alignment: Faces turned away from the focal point appear completely different to a computer. An algorithm is required to normalize the face and make it consistent with the faces in the database. One method is to use a variety of generic face landmarks. Examples include the bottom of the chin, the top of the nose, the outsides of the eyes, various areas surrounding the eyes and lips, and so on. The following step is to train a deep learning system to find these spots on any face and turn it towards the center. This greatly simplifies the face detection process.
3. Measurement and Extraction: This phase involves measuring and extracting numerous features from the face so that the algorithm can compare it to other faces in its database.
4. Recognition: Using the unique measurements of each face, a final deep learning algorithm will compare the measures of each face to known faces in a database. The match will be the closest face in your Database to the measurements of the face in question.
5. Verification: Finally, the deep learning algorithms perform the final act of matching the face with other faces in the database. If the face matches, it is said to be verified, otherwise, it is said to be unverified. This is known as face verification. Faces are compared in it to provide the end result of a lengthy process. However, this step is a slightly complicated step.
What are the Use Cases of Facial Recognition?
There are many industries that are making use of face recognition technology, some of the use cases are:
The phone unlocks: Face recognition is now used to unlock a variety of phones, ranging from the cheapest to the most recent iPhones. This technology is an effective method for securing personal data and ensuring that sensitive data is inaccessible to the offender if the phone is stolen.
Smart Advertising: Face recognition has the potential to make advertising more targeted guesses about people’s age and gender. Companies such as Tesco have already announced plans to install displays with built-in facial recognition at gas stations. It’s only a matter of time before facial recognition is widely used in advertising.
Finding Missing people: Face recognition technology could be used to find missing children and victims of human trafficking. If a missing person is entered into a database, law enforcement can be notified if they are identified by facial recognition in a public space, such as an airport, retail store, or other public areas.
Identification of people on social media: When Facebook users appear in photos, Facebook uses facial recognition technology to identify them instantly. This makes it easier for people to find images in which they appear, and it also allows them to suggest when specific people should be tagged in photos.
How can GTS help you?
Global Technology Solutions has the skills, knowledge, resources, and capacity to provide you with whatever you require in terms of image datasets and image data collection. Our datasets are of excellent quality and are carefully designed to match your needs and solve your problems. We also offer image datasets, Video datasets, Text dataset, and Audio datasets. Our multiple verification methods ensure that we always deliver the finest quality image dataset.
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