Hand and face landmark detection in Computer Vision - Model Metrics & Evaluation

Metrics & Evaluation - Hand and face landmark detection

Which metric matters for Hand and face landmark detection and WHY

For hand and face landmark detection, the key metric is Mean Squared Error (MSE) or Normalized Mean Error (NME). These measure how close the predicted points are to the true points on the hand or face.

We want the predicted landmarks to be as close as possible to the real landmarks, so smaller error means better model.

Sometimes, Percentage of Correct Keypoints (PCK) is used. It counts how many points fall within a certain distance from the true points, showing accuracy in a more intuitive way.

Confusion matrix or equivalent visualization

Landmark detection is a regression task, so confusion matrix does not apply directly.

Instead, we use error distance between predicted and true points. For example:

    True point: (x=50, y=100)
    Predicted point: (x=52, y=98)
    Distance error = sqrt((52-50)^2 + (98-100)^2) = sqrt(4 + 4) = 2.83 pixels
    

We calculate this for all points and average to get MSE or NME.

Precision vs Recall tradeoff with concrete examples

Precision and recall are not typical metrics here because this is not a classification task.

Instead, the tradeoff is between accuracy of landmark localization and model speed or complexity.

For example, a very accurate model might take longer to run, which is bad for real-time apps like video calls.

A faster model might predict landmarks less precisely, causing small errors in applications like gesture control.

Choosing the right balance depends on the use case.

What "good" vs "bad" metric values look like for this use case

Good: Average landmark error less than 5 pixels on a 256x256 image, or PCK above 90% within a small threshold.

This means most points are very close to the true landmarks, so the model is reliable.

Bad: Average error above 15 pixels or PCK below 70% means landmarks are often far from correct spots, causing poor results in applications.

Metrics pitfalls

• Ignoring scale: Measuring error in pixels without normalizing for image size can mislead. Use normalized error.
• Overfitting: Very low error on training data but high error on new images means the model memorizes instead of generalizing.
• Data leakage: Testing on images very similar to training can inflate performance.
• Using classification metrics: Precision and recall do not apply here and can confuse evaluation.

Self-check question

Your hand landmark model has an average normalized error of 0.12 (12%) on test data. Is it good for production? Why or why not?

Answer: An error of 12% means landmarks are on average 12% of the image size away from true points. This is quite high and may cause noticeable mistakes in applications. Usually, errors below 5% are preferred for good quality. So, this model likely needs improvement before production.

Key Result

Mean Squared Error or Normalized Mean Error are key metrics showing how close predicted landmarks are to true points.

Practice

1. What is the main purpose of hand and face landmark detection in computer vision?

easy

A. To compress video files

B. To increase image resolution

C. To change the color of images

D. To find key points on hands and faces in images or videos

Solution

1. Step 1: Understand the goal of landmark detection

   Landmark detection identifies important points on hands and faces to understand their shape and position.

2. Step 2: Compare options with the goal

   Only To find key points on hands and faces in images or videos matches this goal by describing key point detection on hands and faces.

3. Final Answer:

   To find key points on hands and faces in images or videos -> Option D

4. Quick Check:

   Landmark detection = key points detection [OK]

Hint: Landmark detection means finding important points [OK]

Common Mistakes:

• Confusing landmark detection with image enhancement
• Thinking it changes image colors
• Mixing it up with video compression

2. Which of the following is the correct way to import MediaPipe's hand landmark detection module in Python?

easy

A. import mediapipe.solutions.hands as mp_hands

B. import mediapipe.hands as mp_hands

C. import mediapipe as mp
mp.solutions.hands

D. from mediapipe import hands

Solution

1. Step 1: Recall MediaPipe import syntax

   MediaPipe modules are imported from mediapipe.solutions, e.g., mediapipe.solutions.hands.

2. Step 2: Check each option

   import mediapipe.solutions.hands as mp_hands correctly imports mediapipe.solutions.hands as mp_hands. Others are incorrect or incomplete.

3. Final Answer:

   import mediapipe.solutions.hands as mp_hands -> Option A

4. Quick Check:

   Correct import = mediapipe.solutions.hands [OK]

Hint: MediaPipe modules come from mediapipe.solutions [OK]

Common Mistakes:

• Using incorrect import paths
• Trying to import submodules directly without solutions
• Confusing alias names

3. Given the following Python code using MediaPipe for hand landmarks detection, what will be printed?

import mediapipe as mp
mp_hands = mp.solutions.hands
hands = mp_hands.Hands(static_image_mode=True)
results = hands.process(image_rgb)
print(len(results.multi_hand_landmarks))

Assuming image_rgb contains one clear hand.

medium

A. 1

B. Error

C. None

D. 0

Solution

1. Step 1: Understand the code flow

   The code processes an RGB image with one hand using MediaPipe Hands in static mode.

2. Step 2: Interpret the output

   Since one hand is present, results.multi_hand_landmarks will contain one set of landmarks, so its length is 1.

3. Final Answer:

   1 -> Option A

4. Quick Check:

   One hand detected = length 1 [OK]

Hint: Length of landmarks list equals number of detected hands [OK]

Common Mistakes:

• Assuming zero when hand is present
• Confusing None with empty list
• Expecting error without checking input

4. You wrote this code to detect face landmarks but get an error:

import mediapipe as mp
mp_face = mp.solutions.face_mesh
face_mesh = mp_face.FaceMesh()
results = face_mesh.process(image_bgr)
print(results.multi_face_landmarks)

What is the likely cause of the error?

medium

A. Missing import for cv2

B. FaceMesh class does not exist

C. Input image should be RGB, not BGR

D. process() method requires grayscale image

Solution

1. Step 1: Check input image format for MediaPipe FaceMesh

   MediaPipe expects RGB images, but the code uses image_bgr (BGR format).

2. Step 2: Understand error cause

   Using BGR instead of RGB causes wrong color channels and likely errors in detection.

3. Final Answer:

   Input image should be RGB, not BGR -> Option C

4. Quick Check:

   MediaPipe needs RGB input images [OK]

Hint: Always convert BGR to RGB before MediaPipe processing [OK]

Common Mistakes:

• Passing BGR images directly
• Assuming FaceMesh class is missing
• Thinking grayscale is required

5. You want to build a gesture recognition app using hand landmarks. Which approach best improves accuracy when hands are rotated or partially hidden?

hard

A. Only train on perfectly centered and clear hand images

B. Use data augmentation with rotated and occluded hand images during training

C. Ignore landmarks and use raw images directly

D. Use grayscale images instead of color

Solution

1. Step 1: Understand challenges in gesture recognition

   Hands can appear rotated or partly hidden, so model must handle variations.

2. Step 2: Choose best method to improve robustness

   Data augmentation with rotated and occluded images teaches model to recognize gestures despite changes.

3. Final Answer:

   Use data augmentation with rotated and occluded hand images during training -> Option B

4. Quick Check:

   Augmentation improves model robustness [OK]

Hint: Augment training data to handle rotations and occlusions [OK]

Common Mistakes:

• Training only on perfect images
• Ignoring landmarks reduces accuracy
• Using grayscale loses important info