The Face Recognition Data Fetch API allows you to send an image and receive detailed information about the objects detected, including their exact location using coordinates (x, y, width, height) and key points (landmarks) that describe specific characteristics of the object, such as facial features, body joints, or relevant object details.
Thanks to state-of-the-art detection and segmentation algorithms, the API accurately identifies each object within the image and calculates its landmarks, enabling applications such as motion tracking, facial recognition, pose analysis, augmented reality, smart editors, and security systems. Each response includes an operation status, a message, and an array of results with absolute coordinates, ensuring that developers can immediately integrate the information into their applications.
The API is designed to support images in various formats and resolutions, ensuring consistency and accuracy in the results.
In short, the Face Recognition Data Fetch API offers a comprehensive solution for any project that requires image analysis with precise coordinates and key points. Whether for facial recognition, motion tracking, pose analysis, augmented reality, or advanced computer vision, this API delivers fast, consistent, and highly accurate results, facilitating the development of intelligent and visually advanced applications.
Receives an image and returns coordinates, facilitating face detection.
Face Detection - Endpoint Features
| Object | Description |
|---|---|
Request Body |
[Required] Json |
{"status": 0, "message": "", "result": [{"x": 96, "y": 48, "w": 79, "h": 79, "landmark": [{"x": 99, "y": 76}, {"x": 99, "y": 86}, {"x": 100, "y": 96}, {"x": 102, "y": 105}, {"x": 105, "y": 114}, {"x": 111, "y": 121}, {"x": 119, "y": 127}, {"x": 125, "y": 132}, {"x": 133, "y": 134}, {"x": 140, "y": 133}, {"x": 146, "y": 127}, {"x": 152, "y": 121}, {"x": 157, "y": 114}, {"x": 161, "y": 107}, {"x": 163, "y": 99}, {"x": 164, "y": 91}, {"x": 165, "y": 83}, {"x": 109, "y": 77}, {"x": 113, "y": 71}, {"x": 120, "y": 69}, {"x": 127, "y": 71}, {"x": 134, "y": 74}, {"x": 143, "y": 75}, {"x": 149, "y": 73}, {"x": 155, "y": 73}, {"x": 161, "y": 75}, {"x": 162, "y": 81}, {"x": 139, "y": 79}, {"x": 139, "y": 85}, {"x": 139, "y": 90}, {"x": 139, "y": 96}, {"x": 130, "y": 98}, {"x": 133, "y": 100}, {"x": 137, "y": 102}, {"x": 141, "y": 101}, {"x": 144, "y": 99}, {"x": 117, "y": 79}, {"x": 121, "y": 77}, {"x": 125, "y": 77}, {"x": 128, "y": 80}, {"x": 124, "y": 80}, {"x": 121, "y": 79}, {"x": 145, "y": 81}, {"x": 149, "y": 79}, {"x": 153, "y": 80}, {"x": 156, "y": 82}, {"x": 152, "y": 82}, {"x": 149, "y": 82}, {"x": 119, "y": 107}, {"x": 125, "y": 106}, {"x": 132, "y": 106}, {"x": 136, "y": 108}, {"x": 139, "y": 107}, {"x": 144, "y": 107}, {"x": 148, "y": 109}, {"x": 143, "y": 115}, {"x": 139, "y": 118}, {"x": 135, "y": 118}, {"x": 130, "y": 118}, {"x": 124, "y": 114}, {"x": 121, "y": 107}, {"x": 132, "y": 108}, {"x": 135, "y": 108}, {"x": 139, "y": 108}, {"x": 146, "y": 109}, {"x": 139, "y": 114}, {"x": 135, "y": 115}, {"x": 131, "y": 114}]}]}
curl --location --request POST 'https://zylalabs.com/api/11339/face+recognition+data+fetch+api/21478/face+detection' --header 'Authorization: Bearer YOUR_API_KEY'
--data-raw '{
"url": "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcT6Fo-gbghS19ipMBMeqD0HtEjHDnCHkIpfcg&s"
}'
| Header | Description |
|---|---|
Authorization
|
[Required] Should be Bearer access_key. See "Your API Access Key" above when you are subscribed. |
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The Face Detection endpoint returns data including the status of the operation, a message, and an array of detected faces. Each face entry contains coordinates (x, y, width, height) and landmarks that represent specific facial features.
The key fields in the response data include "status" (indicating success or failure), "message" (providing additional information), and "result" (an array of detected faces with their coordinates and landmarks).
The response data is structured as a JSON object. It includes a top-level object with "status," "message," and "result." The "result" field contains an array of objects, each representing a detected face with its coordinates and landmarks.
The Face Detection endpoint provides information on detected faces, including their bounding box coordinates (x, y, width, height) and detailed landmark coordinates that describe facial features.
The Face Detection endpoint accepts an image file as input. Users can customize their requests by specifying the image format and resolution, ensuring compatibility with the API's processing capabilities.
Users can utilize the returned data by extracting the coordinates and landmarks for each detected face. This information can be integrated into applications for facial recognition, tracking, or augmented reality features.
Data accuracy is maintained through the use of state-of-the-art detection and segmentation algorithms. Continuous updates and improvements to these algorithms help ensure high precision in object and landmark detection.
Typical use cases include facial recognition systems, motion tracking applications, pose analysis for fitness apps, augmented reality experiences, and security systems that require real-time face detection and analysis.
The Face Detection endpoint supports various image formats, including JPEG, PNG, and BMP. It can handle images of different resolutions, ensuring flexibility for developers to integrate the API into diverse applications.
Users can customize requests by specifying the image format and resolution. This allows for optimized processing based on the specific needs of their application, ensuring better performance and accuracy.
The "landmark" data in the response represents specific facial features, such as eyes, nose, and mouth positions. Each landmark is provided as coordinates, allowing for detailed analysis of facial expressions and orientations.
The "status" field indicates the success or failure of the request. A status of 0 typically means success, while any other value indicates an error, prompting users to check the accompanying "message" for details.
If the response contains no detected faces, users should check the input image quality and ensure it meets the API's requirements. Low resolution or poor lighting may affect detection accuracy.
Advanced applications include real-time facial recognition for security systems, emotion detection in marketing analytics, and interactive augmented reality experiences that respond to user expressions.
Users can ensure optimal results by providing high-quality images with good lighting and clear visibility of faces. Additionally, using images that conform to the supported formats and resolutions enhances detection accuracy.
The "message" field provides additional context about the operation's outcome. It may include error descriptions or confirmations of successful processing, helping users troubleshoot or validate their requests.
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