This content originally appeared on DEV Community and was authored by Ertugrul
"Faces don’t lie — but landmarks sometimes do."
🚀 Project Idea
Hey there! In this post, I’ll share my journey of building a Face Landmark Detection pipeline using OpenCV DNN and Facemark LBF. The system takes a raw video as input, detects faces, extracts 68 facial landmarks, smooths them across frames, and finally outputs:
- an annotated video with landmarks and bounding boxes
- an optional CSV file with landmark coordinates for every frame
The idea was simple:
"Take a face → get the points."
But to make it robust, I had to mix deep learning detection with classical landmarking and add a touch of signal processing.
🧱 Code Structure
The project is split into modular components:
-
detector.py→ Loads and runs the DNN-based face detector (SSD ResNet) -
landmarks.py→ Drawing utilities for the 68-point facial structure -
helpers.py→ Video I/O, CSV logging, smoothing, and per-frame pipeline -
main.py→ Entry point to run the full pipeline
🔍 Step 1 — Face Detection
I used OpenCV’s Deep Neural Network (DNN) SSD ResNet model. The detector takes each frame, converts it into a blob, and feeds it into the Caffe network:
blob = cv.dnn.blobFromImage(cv.resize(frame, (300, 300)), 1.0, (300, 300),
(104.0, 177.0, 123.0), False, False)
self.net.setInput(blob)
detections = self.net.forward()
This gives us bounding boxes with confidence scores. I kept only the ones above a threshold (conf_thr=0.6).
🎯 Step 2 — Landmark Extraction
With face boxes ready, I used Facemark LBF to extract the 68 landmark points:
facemark = cv.face.createFacemarkLBF()
facemark.loadModel(LBF_MODEL)
ok, landmarks = facemark.fit(frame, np.array(boxes))
This returns arrays shaped (68, 2) → coordinates for jawline, eyebrows, eyes, nose, and lips.
📉 Step 3 — Landmark Smoothing
Raw landmarks jitter a lot between frames. To stabilize them, I applied an Exponential Moving Average (EMA):
if prev_pts is None:
smooth = pts.copy()
else:
smooth = alpha * prev_pts + (1.0 - alpha) * pts
This keeps the motion natural but removes frame-by-frame noise.
🖼️ Step 4 — Drawing the Mesh
I grouped the 68 points into face regions and connected them with polylines:
- Jawline
- Eyebrows
- Nose bridge & base
- Eyes
- Inner & outer lips
for (x, y) in pts:
cv.circle(frame, (int(x), int(y)), 1, (0, 255, 0), -1)
The result? A clear, real-time facial mesh overlay.
đź“‚ Outputs
Annotated Video:
Watch on YouTubeCSV Example:
frame_idx,x0,x1,...,y66,y67
0,123,130,...,200,205
1,124,129,...,199,206
This makes the system useful both for visualization and downstream ML tasks.
đź“Ś Lessons Learned
- DNN face detection is robust, but combining it with traditional landmarking is still effective.
- Smoothing is mandatory — raw landmarks are too noisy for real use.
- CSV logging adds value for research/analytics beyond just visualization.
đź“‚ GitHub Repository
You can find the full code here:
đź”— GitHub: Face Landmarks Detection
"A single face in a frame is simple — but tracking it smoothly across time is where the real challenge begins."
This content originally appeared on DEV Community and was authored by Ertugrul
Ertugrul | Sciencx (2025-08-20T12:13:04+00:00) # 🎯 Face Landmarks Detection (OpenCV DNN + Facemark). Retrieved from https://www.scien.cx/2025/08/20/%f0%9f%8e%af-face-landmarks-detection-opencv-dnn-facemark/
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