Hand and face landmark detection in Computer Vision - Model Pipeline Trace

Model Pipeline - Hand and face landmark detection

This pipeline detects key points on hands and faces in images. It finds landmarks like fingertips and facial features to understand pose and expressions.

Data Flow - 5 Stages

1Input Image

1 image x 256 x 256 x 3 (RGB)→Load and resize image to fixed size→1 image x 256 x 256 x 3

Photo of a person with hands visible, resized to 256x256 pixels

↓

2Preprocessing

1 image x 256 x 256 x 3→Normalize pixel values to range [0,1]→1 image x 256 x 256 x 3

Pixel values converted from 0-255 to 0.0-1.0

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3Feature Extraction

1 image x 256 x 256 x 3→Apply convolutional layers to extract visual features→1 tensor x 64 x 64 x 64 (feature maps)

Edges and textures detected in image regions

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4Landmark Regression Head

1 tensor x 64 x 64 x 64→Fully connected layers predict landmark coordinates→1 vector x 63 (21 hand landmarks x 3 coords)

Coordinates like (x=0.45, y=0.32, z=0.05) for each hand landmark

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5Postprocessing

1 vector x 63→Scale normalized coordinates back to image size→1 vector x 63

Landmark at pixel (115, 82, depth 13) on 256x256 image

Training Trace - Epoch by Epoch

Loss
0.12 |*       
0.10 | *      
0.08 |  *     
0.06 |   *    
0.04 |    *   
0.02 |     *  
0.00 +--------
       1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.12	0.65	Model starts learning basic landmark positions
2	0.08	0.75	Loss decreases as model improves landmark precision
3	0.05	0.82	Model captures hand and face shapes better
4	0.035	0.88	Fine details like finger joints detected more accurately
5	0.025	0.91	Training converges with stable low loss and high accuracy

Prediction Trace - 4 Layers

Layer 1: Input Image

Layer 2: Convolutional Feature Extraction

Layer 3: Landmark Regression

Layer 4: Postprocessing

Model Quiz - 3 Questions

What does the feature extraction stage mainly do?

A Detect edges and textures in the image

B Normalize pixel values

C Scale landmark coordinates to image size

D Predict landmark coordinates directly

Key Insight

This visualization shows how a model learns to find detailed points on hands and faces by extracting image features and refining predictions over time. The decreasing loss and increasing accuracy confirm the model's improving understanding of landmark positions.