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Build recognition system. Detection, verification. CV project 2024.
Project Overview
Build a complete face recognition pipeline that detects faces in images/video, generates face embeddings, and identifies individuals against a registered database. This project covers the full CV pipeline: detection, alignment, feature extraction, and matching—skills directly applicable to security systems, attendance tracking, and photo organization.
System Architecture
| Output | [(x, y, w, h), ...] |
| CNN maps aligned face | 128/512-dim vector |
| Similar faces | nearby vectors |
| Nearest neighbor | identity |
| Threshold | known/unknown decision |
Implementation Steps
Step 1: Face Detection
Example
# Using MTCNN (Multi-task Cascaded CNN)
from mtcnn import MTCNN
detector = MTCNN()
def detect_faces(image):
results = detector.detect_faces(image)
faces = []
for result in results:
box = result['box'] # [x, y, width, height]
confidence = result['confidence']
landmarks = result['keypoints'] # eyes, nose, mouth
if confidence > 0.95:
faces.append({
'box': box,
'landmarks': landmarks,
'confidence': confidence
})
return faces
Step 2: Face Alignment
Example
def align_face(image, landmarks, output_size=(160, 160)):
# Define target landmark positions (canonical face)
target_left_eye = (0.35 * output_size[0], 0.35 * output_size[1])
target_right_eye = (0.65 * output_size[0], 0.35 * output_size[1])
# Compute affine transform
src_points = np.array([landmarks['left_eye'], landmarks['right_eye']])
dst_points = np.array([target_left_eye, target_right_eye])
# Apply rotation and scaling to normalize face pose
M = cv2.getAffineTransform(
np.float32([src_points[0], src_points[1], landmarks['nose']]),
np.float32([dst_points[0], dst_points[1], target_nose])
)
aligned = cv2.warpAffine(image, M, output_size)
return aligned
Step 3: Embedding Extraction
Example
# Using FaceNet or ArcFace model
from tensorflow.keras.models import load_model
class FaceEmbedder:
def __init__(self):
self.model = load_model('facenet_keras.h5')
def get_embedding(self, aligned_face):
# Preprocess: normalize pixel values
face = aligned_face.astype('float32')
face = (face - 127.5) / 128.0
face = np.expand_dims(face, axis=0)
# Extract 128-dimensional embedding
embedding = self.model.predict(face)[0]
# L2 normalize
embedding = embedding / np.linalg.norm(embedding)
return embedding # Unit vector in 128-dim space
Step 4: Face Matching
Example
class FaceDatabase:
def __init__(self, threshold=0.6):
self.embeddings = {} # {person_name: [embedding1, embedding2, ...]}
self.threshold = threshold
def register(self, name, embedding):
if name not in self.embeddings:
self.embeddings[name] = []
self.embeddings[name].append(embedding)
def identify(self, query_embedding):
best_match = None
best_distance = float('inf')
for name, stored_embeddings in self.embeddings.items():
for stored in stored_embeddings:
# Euclidean distance (or cosine similarity)
distance = np.linalg.norm(query_embedding - stored)
if distance < best_distance:
best_distance = distance
best_match = name
if best_distance < self.threshold:
return best_match, best_distance
return "Unknown", best_distance
Training Face Embeddings (Triplet Loss)
| Triplet | (anchor, positive, negative) |
| anchor | Face of person A |
| positive | Different photo of person A |
| negative | Face of person B |
| If positive is closer than negative by at least margin | loss = 0 |
| Otherwise | gradient pushes embeddings apart/together |
Real-Time Video Pipeline
Example
def real_time_recognition(camera_id=0):
cap = cv2.VideoCapture(camera_id)
while True:
ret, frame = cap.read()
faces = detect_faces(frame)
for face_info in faces:
# Crop and align
aligned = align_face(frame, face_info['landmarks'])
# Get embedding
embedding = embedder.get_embedding(aligned)
# Identify
name, confidence = database.identify(embedding)
# Draw result
draw_box_and_label(frame, face_info['box'], name, confidence)
cv2.imshow('Face Recognition', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
Performance Considerations
| Detection speed | MTCNN ~30ms/frame, RetinaFace ~15ms |
| Embedding extraction | FaceNet ~10ms/face (GPU) |
| Matching | Linear search O(n), use FAISS for large databases |
| For 1000 registered people | real-time on modest GPU |
| For 1M+ people | need approximate nearest neighbors (ANN) |
| LFW (Labeled Faces in Wild) | 99.6% accuracy |
| MegaFace | 98.5% at 1M distractors |
Ethical Considerations and Privacy
Concerns
- Consent: People should know they're being recognized
- Bias: Models perform worse on some demographics
- Surveillance: Enabling mass tracking raises civil liberties issues
- Data security: Face embeddings are biometric data
Responsible development
- Test for demographic bias (accuracy across groups)
- Implement opt-out mechanisms
- Encrypt stored embeddings
- Clear data retention policies
- Follow regulations (GDPR, CCPA)
Interview Questions
Q: How do you handle face recognition with masks or occlusion? A: Use models trained on partially occluded faces (periocular recognition—focus on eye region). ArcFace and newer models include masked-face training data. For severe occlusion, fall back to other biometrics or require unmasking. Detection models can be retrained to locate visible face regions.
Q: What is the difference between face verification and face identification? A: Verification (1:1): "Is this person who they claim to be?" Compare one face against one stored template. Used for phone unlock, passport control. Identification (1:N): "Who is this person?" Compare against entire database. Used for surveillance, attendance. Identification is harder—scales with database size and requires better embeddings.
Exam Focus
Revise definitions, diagrams, examples, and short-answer points for Face Recognition System — Artificial Intelligence.
Interview Use
Prepare one clear explanation, one practical example, and one common mistake for this Artificial Intelligence topic.
Search Terms
artificial-intelligence, artificial intelligence, artificial, intelligence, projects, face, recognition, system
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