Face Detection

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Instructions
Input - Click on the “+” button in the video player and upload a decent resolution video with less than 40mb.
Output - Provides an image or a video with the faces of the people localized. Can detect multiple faces present in the image or video with face location (XY coordinates) in a JSON format.
Note: Upload the appropriate video for optimal performance. Video that doesn't relate to the model might provide you unexpected results.

Face detection is a computer vision task to detect the faces in an image or video and return the location of the face. The application localizes the faces by drawing a bounding box around the individual faces. Detecting faces in an image or a video is not easy for humans at every single frame, It gets complex as the requirement for identification and other details are demanded, given the dynamic nature of faces. For example, faces must be detected regardless of orientation or angle they are facing, light levels, clothing, accessories, hair color, facial hair, makeup, age, and so on.

During this Covid 19 pandemic, life has its own challenges. The ability to detect a person’s face and maintain discipline is absolutely challenging. The Face detection system can be used as a backbone for a face recognition system to detect and identify the person. It is used to detect faces in real time for surveillance and tracking of persons or objects. It is widely used in cameras to identify multiple appearances in the frame. Not only this can be extended to even search for a person in a crowd with location, date and time. This detection is a key factor for doing any further facial analysis when combined with other algorithms.

Use Cases

Face Recognition, Security System, Finding Missing Person, Protecting Law Enforcements, Track Attendance, Count People Paying Attention, Biometrics, and so on.

API Request and Response

TensorGo Platform provides you with a one stop solution to customize the our API offerings as per your use case by mixing and matching the existing APIs or requesting for a new custom model. This accelerates the development of use cases with minimal or no code towards deep learning applications.
The endpoint for API is:
[URL]
Request:
post/
[URL]
	body:{
		id:”Some Unique  ID”
		app:”Name of the app”
		file:”Attached File”
             }
Example:
post/
	[URL]
	body:{
		id:xsk231ds168wd
		app:faceDetect
		file:”Attached File”
             }

Response:
	{ msg:Uploaded }

Download the Uploaded video:
get/
	[URL]
	body:{
		id:xsk231ds168wd
		app:faceDetect
             }