Facial recognition is one of the emerging technologies that has been acknowledged at a global level because of its compelling attributes that make the facial verification process frictionless and promising as compared to other methods. Along with a number of advantages, this technology also embraces several techniques for recognition purposes which play a significant role in the effectiveness of the verification process.
This aspect additionally strengthens the scope of face verification because of the diversity it allows to incorporate for effective results. There are various techniques that are used in these systems which help to strengthen their functioning. Some of those techniques are discussed below.
This is a technique applied in facial recognition system which takes the whole face as an image to make eigenfaces. Eigen is a vector technique that is incorporated into computer vision of faces for identification purposes. In this technique, a set of images is inserted into the database and the features are extracted accordingly. The faces are lined up to align the mouths and eyes. Later, the eigenfaces are extracted from the data that was inserted. In this way, faces are recognized because the images act as weights and the system accepts the commands according to the weight of images incorporated in eigenfaces.
One of the biggest drawbacks of this technique is that the system does not identify the face that falls below the set weights. That is if a certain face is over the limit of the assigned weight, the system labels it unidentified. Thus, this technique lacks the aspect of generalizability. It means that only specific faces would be detected which would fulfill the criteria set in the system.
Face verification systems are designed in accordance with the human facial verification features that should be detected when a face interacts with the system. These features include eyes, mouth, and nose. This technique poses a hindrance in the process of verification during feature restoration. That is, the verification system may also try to detect those portions of the face that are actually out of range for detection. For instance, the head of a human could not be detected due to large variations.
Another technique used in feature-based verification is the consideration of lines and curves that naturally appear on a face. This technique focuses on the sharp features of a human face to determine angular positions.
In this technique, the system models a face using 2D and 3D technology. Here, the face is analyzed through the set parameters of the model. The sample face image is then recognized accordingly. The 3D feature allows the modeling technique to capture different facial verification attributes.
In this technique, the system uses both methods of feature extraction and holistic mapping of the face. It captures the face in a 3D format considering the eyes, chin, or the shape of eye sockets. In this technique, even a profile image is enough for verification as it allows the system to detect the face through the axis of measurement and assess the depth of the face.
Google introduced FaceNet in 2015 which comprised of an artificial neural network and an algorithm. This technique is used in Google photos and in comparison, to the already prevailing labeled systems, this technology provided an accuracy of 99.63%.
In the area of facial recognition services, Amazon also developed a cloud-based face verification system named ‘Rekognition’. This system was competitively able to detect 100 faces in one image at a time. Also, it could detect millions of images through the provided databases. But this software failed because, during a face recognition process, it is reported that this system falsely identified 28 US congress members as people arrested for criminal activities.
The two leading business giants in their respective domains developed a facial recognition software named ‘Face ++’. According to the research of MIT, This software turned out to be a disaster because it appeared to have high error rates when verifying dark-skinned women as compared to white-skinned women.
Facial verification software solutions are designed to ease the process of authentication. Any kind of glitches in the AI-supported software can result in questions that may harm the reputation of IDV solution providers. This technology is overtaking the identification process, benefits of facial recognition is immense. This is the reason that when these solutions are developed, they are thoroughly tested before being introduced into the live environment. Still, these entities should focus on continuous improvement because machine-supported software is always subjected to error no matter how much accuracy is ascertained. Many IDV services are striving to bring compelling benefits to businesses by applying the right strategic combination of algorithms in facial recognition systems that can ensure maximum safety.
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