Detect faces, bounding boxes, and five facial landmarks (eyes, nose, mouth corners) in any image via a simple REST API. Upload a JPEG or PNG, get structured JSON back. 5,000 free requests/month.
- Detect every face in an image with confidence scores
- Get normalized bounding boxes and five facial landmark coordinates per face
- Supports JPEG and PNG uploads up to 16 MB
- All coordinates normalized (0–1) — resolution-independent
- 5,000 requests/month on free tier
- Example Response:
{
"image_width": 612,
"image_height": 408,
"total_faces": 2,
"faces": [
{
"confidence": 0.9991222023963928,
"bounding_box": {
"origin_x": 0.7696828591947755,
"origin_y": 0.07557892352342604,
"span_width": 0.18395689328511555,
"span_height": 0.27593533992767333
},
"landmarks": {
"left_eye": { "x": 0.803, "y": 0.168 },
"right_eye": { "x": 0.890, "y": 0.154 },
"nose": { "x": 0.835, "y": 0.182 },
"mouth_left": { "x": 0.808, "y": 0.264 },
"mouth_right": { "x": 0.886, "y": 0.253 }
}
}
]
}Create an account at omkar.cloud to get your API key.
It takes just 2 minutes to sign up. You get 5,000 free requests every month — more than enough to build and ship a face detection feature without paying a dime.
This is a well built product, and your search for the best Face Detection API ends right here.
curl -X POST "https://face-detection-api.omkar.cloud/face/analyze" \
-H "API-Key: YOUR_API_KEY" \
-F "image=@photo.jpg"{
"image_width": 612,
"image_height": 408,
"total_faces": 2,
"faces": [
{
"confidence": 0.9991222023963928,
"bounding_box": {
"origin_x": 0.7696828591947755,
"origin_y": 0.07557892352342604,
"span_width": 0.18395689328511555,
"span_height": 0.27593533992767333
},
"landmarks": {
"left_eye": { "x": 0.8030567288398743, "y": 0.16781179904937743 },
"right_eye": { "x": 0.889804744720459, "y": 0.15399449467658993 },
"nose": { "x": 0.8354169130325317, "y": 0.18160911798477172 },
"mouth_left": { "x": 0.8082399487495423, "y": 0.26384876668453217 },
"mouth_right": { "x": 0.886027567088604, "y": 0.25258797258138654 }
}
}
]
}pip install requestsimport requests
response = requests.post(
"https://face-detection-api.omkar.cloud/face/analyze",
headers={"API-Key": "YOUR_API_KEY"},
files={"image": open("photo.jpg", "rb")}
)
print(response.json())POST https://face-detection-api.omkar.cloud/face/analyze
| Parameter | Required | Default | Description |
|---|---|---|---|
image |
Yes | — | JPEG or PNG image file, sent as multipart form-data. Max 16 MB. |
import requests
response = requests.post(
"https://face-detection-api.omkar.cloud/face/analyze",
headers={"API-Key": "YOUR_API_KEY"},
files={"image": open("photo.jpg", "rb")}
)
print(response.json())Sample Response (click to expand)
{
"image_width": 612,
"image_height": 408,
"total_faces": 2,
"faces": [
{
"confidence": 0.9991222023963928,
"bounding_box": {
"origin_x": 0.7696828591947755,
"origin_y": 0.07557892352342604,
"span_width": 0.18395689328511555,
"span_height": 0.27593533992767333
},
"landmarks": {
"left_eye": { "x": 0.8030567288398743, "y": 0.16781179904937743 },
"right_eye": { "x": 0.889804744720459, "y": 0.15399449467658993 },
"nose": { "x": 0.8354169130325317, "y": 0.18160911798477172 },
"mouth_left": { "x": 0.8082399487495423, "y": 0.26384876668453217 },
"mouth_right": { "x": 0.886027567088604, "y": 0.25258797258138654 }
}
},
{
"confidence": 0.9932157397270203,
"bounding_box": {
"origin_x": 0.47802788168191923,
"origin_y": 0.1437148481607437,
"span_width": 0.23806619644165045,
"span_height": 0.35709929466247564
},
"landmarks": {
"left_eye": { "x": 0.5511994868516922, "y": 0.25441014766693115 },
"right_eye": { "x": 0.6608513176441193, "y": 0.2665567696094513 },
"nose": { "x": 0.608675591647625, "y": 0.3018725097179413 },
"mouth_left": { "x": 0.5523210883140565, "y": 0.39049973487854 },
"mouth_right": { "x": 0.6461554944515229, "y": 0.4011178731918335 }
}
}
]
}response = requests.post(
"https://face-detection-api.omkar.cloud/face/analyze",
headers={"API-Key": "YOUR_API_KEY"},
files={"image": open("photo.jpg", "rb")}
)
if response.status_code == 200:
data = response.json()
elif response.status_code == 400:
# Invalid image format or missing file
pass
elif response.status_code == 401:
# Invalid API key
pass
elif response.status_code == 429:
# Rate limit exceeded
passFace & Landmark Detection returns per image:
- Image dimensions (
image_width,image_height) - Total number of faces detected (
total_faces)
Per face:
- Confidence score (0–1)
- Bounding box with origin coordinates and dimensions (
origin_x,origin_y,span_width,span_height) - Five facial landmarks:
left_eye,right_eye,nose,mouth_left,mouth_right— each with normalizedxandycoordinates
All coordinates are normalized between 0 and 1 relative to image dimensions. Multiply by image_width or image_height to get pixel values.
Deep learning model with over 99% confidence on clearly visible faces. Every API call runs inference in real time.
The model handles varied lighting, angles, and partial occlusions. Confidence scores tell you exactly how sure the model is about each detection.
No. All coordinates are normalized between 0 and 1. This makes them resolution-independent.
To convert to pixels, multiply x values by image_width and y values by image_height. For example, if left_eye.x is 0.803 and image_width is 612, the pixel position is 612 × 0.803 ≈ 491.
Yes. The API detects every face in the image. A group photo with 10 people returns 10 face objects, each with its own bounding box, confidence score, and landmarks.
- Left eye — center of the left eye
- Right eye — center of the right eye
- Nose — tip of the nose
- Mouth left — left corner of the mouth
- Mouth right — right corner of the mouth
These five points are enough for face alignment, gaze estimation, expression analysis, and face-aware cropping.
| Plan | Price | Requests/Month |
|---|---|---|
| Free | $0 | 5,000 |
| Starter | $25 | 100,000 |
| Grow | $75 | 1,000,000 |
| Scale | $150 | 10,000,000 |
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