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Attributes

Facial attribute analysis for age, gender, race, and emotion detection.

Available Models

Model Attributes Size Notes
AgeGender Age, Gender 8 MB Exact age prediction
FairFace Gender, Age Group, Race 44 MB Balanced demographics
Emotion 7-8 emotions 2 MB Requires PyTorch

AgeGender

Predicts exact age and binary gender.

Basic Usage

from uniface import RetinaFace, AgeGender

detector = RetinaFace()
age_gender = AgeGender()

faces = detector.detect(image)

for face in faces:
    result = age_gender.predict(image, face.bbox)
    print(f"Gender: {result.sex}")  # "Female" or "Male"
    print(f"Age: {result.age} years")

Output

# AttributeResult fields
result.gender     # 0=Female, 1=Male
result.sex        # "Female" or "Male" (property)
result.age        # int, age in years
result.age_group  # None (not provided by this model)
result.race       # None (not provided by this model)

FairFace

Predicts gender, age group, and race with balanced demographics.

Basic Usage

from uniface import RetinaFace, FairFace

detector = RetinaFace()
fairface = FairFace()

faces = detector.detect(image)

for face in faces:
    result = fairface.predict(image, face.bbox)
    print(f"Gender: {result.sex}")
    print(f"Age Group: {result.age_group}")
    print(f"Race: {result.race}")

Output

# AttributeResult fields
result.gender     # 0=Female, 1=Male
result.sex        # "Female" or "Male"
result.age        # None (not provided by this model)
result.age_group  # "20-29", "30-39", etc.
result.race       # Race/ethnicity label

Race Categories

Label
White
Black
Latino Hispanic
East Asian
Southeast Asian
Indian
Middle Eastern

Age Groups

Group
0-2
3-9
10-19
20-29
30-39
40-49
50-59
60-69
70+

Emotion

Predicts facial emotions. Requires PyTorch.

Optional Dependency

Emotion detection requires PyTorch. Install with: 

pip install torch

Basic Usage

from uniface import RetinaFace
from uniface.attribute import Emotion
from uniface.constants import DDAMFNWeights

detector = RetinaFace()
emotion = Emotion(model_name=DDAMFNWeights.AFFECNET7)

faces = detector.detect(image)

for face in faces:
    result = emotion.predict(image, face.landmarks)
    print(f"Emotion: {result.emotion}")
    print(f"Confidence: {result.confidence:.2%}")

Emotion Classes

Label
Neutral
Happy
Sad
Surprise
Fear
Disgust
Anger
Label
Neutral
Happy
Sad
Surprise
Fear
Disgust
Anger
Contempt

Model Variants

from uniface.attribute import Emotion
from uniface.constants import DDAMFNWeights

# 7-class emotion
emotion = Emotion(model_name=DDAMFNWeights.AFFECNET7)

# 8-class emotion
emotion = Emotion(model_name=DDAMFNWeights.AFFECNET8)

Combining Models

Full Attribute Analysis

from uniface import RetinaFace, AgeGender, FairFace

detector = RetinaFace()
age_gender = AgeGender()
fairface = FairFace()

faces = detector.detect(image)

for face in faces:
    # Get exact age from AgeGender
    ag_result = age_gender.predict(image, face.bbox)

    # Get race from FairFace
    ff_result = fairface.predict(image, face.bbox)

    print(f"Gender: {ag_result.sex}")
    print(f"Exact Age: {ag_result.age}")
    print(f"Age Group: {ff_result.age_group}")
    print(f"Race: {ff_result.race}")

Using FaceAnalyzer

from uniface import FaceAnalyzer

analyzer = FaceAnalyzer(
    detect=True,
    recognize=False,
    attributes=True  # Uses AgeGender
)

faces = analyzer.analyze(image)

for face in faces:
    print(f"Age: {face.age}, Gender: {face.sex}")

Visualization

import cv2

def draw_attributes(image, face, result):
    """Draw attributes on image."""
    x1, y1, x2, y2 = map(int, face.bbox)

    # Draw bounding box
    cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)

    # Build label
    label = f"{result.sex}"
    if result.age:
        label += f", {result.age}y"
    if result.age_group:
        label += f", {result.age_group}"
    if result.race:
        label += f", {result.race}"

    # Draw label
    cv2.putText(
        image, label, (x1, y1 - 10),
        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2
    )

    return image

# Usage
for face in faces:
    result = age_gender.predict(image, face.bbox)
    image = draw_attributes(image, face, result)

cv2.imwrite("attributes.jpg", image)

Accuracy Notes

Model Limitations

  • AgeGender: Trained on CelebA; accuracy varies by demographic
  • FairFace: Trained for balanced demographics; better cross-racial accuracy
  • Emotion: Accuracy depends on facial expression clarity

Always test on your specific use case and consider cultural context.

Next Steps