This kind of application provides an easier human-machine interaction routine in concern to the face detection and recognition technology With the help of a regular web camera, a machine can detect and recognize a person’s face from the previously-stored database of the faces.
However, face detection can have very useful applications.
To provide the set of the detection algorithm, behind that algorithm, specific architectures works.
These algorithms have got to provide at least a 95% successful cognizance rate, out of which less than 3% of the detected faces are false positives.
It will take at least 3-4 years for facial recognition to come in complete correspondence with human rights and one’s privacy.
The facial recognition process generally involves two stages.
The specific processor Algorithm to get the specific, more details about a person’s face.
Face Detection where the picture is searched to find a face, then the picture is processed to crop and extract the person’s face for easier recognition.
Face Recognition where that detected and processed face is compared with a database of known faces, for outcomes who’s that person.
Live or video images of a scene identify or verify one or more persons in the scene using a stored database of faces.
The input to the system is an unknown face and the system reports back the decided identity from a database of known individuals whereas in verification problems the system needs to confirm or reject the claimed identity of the input face.
Uncontrolled video images posing a wide range of different technical challenges.
The simple case with small rotation angles.
The most commonly addressed case when there are frontal and rotated images.
Breach of privacy
It can track people like you: anytime, anywhere. But also helps Government to catch criminals.
Enormous data storage
Machine learning technology requires massive data sets to “learn” to deliver accurate results. So there needs to be enormous storage.
Danger in recognition
You cannot guarantee that a person will stand still and face the camera, so the results will not always be correct.
We hope you learn something new in this AI / ML Based Facial Detection and Recognition Blog.
We have to throw out simple and efficient techniques for processing and recognizing face objects and applying algorithms that perform better as per requirement.
The characteristics of these techniques are very suitable for many applications.
We first identified two key issues in the face recognition literature the illumination and pose problems We then examined existing methods of handling these two problems extensively.
These client testimonies are straight from the horse’s mouth. We do not claim to be great in our service offerings, our clients do. Here are some of them:
Infistack Technologies PVT LTD demonstrated a hands-on approach in developing the application. The client lauded their attentiveness to details However, despite the positive remarks on the team's workflow, the project was unfortunately discontinued.
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Infistack Technologies Pvt Ltd's engagement has resulted in an excellent outcome. They have managed to fix the issues on the client's portals and implemented new significant features. The team has executed a seamless workflow through adequate resources for communication, tracking, and time.
Despite their code having room for improvement, Infistack Technologies PVT LTD was able to deliver the requested final product. The team also upheld great communications and was very reactive during the project.
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Infistack Technologies PVT LTD’s efforts improved the client’s reporting processes, increasing productivity fivefold. They fostered a smooth workflow through a collaborative and flexible approach. Beyond their top-notch project management, their technical expertise and guidance were impressive.