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"""
Facial embedding matcher for identity verification.
This module provides comprehensive facial recognition functionality including
face detection, embedding extraction, and similarity comparison. It serves
as the core facial matching component for the identity validation system.
"""
import os
import logging
import tempfile
from typing import List, Dict, Any, Optional, Tuple
from datetime import datetime, timezone
import numpy as np
logger = logging.getLogger(__name__)
class FaceDetector:
"""
Face detection component for identifying faces in images.
This class handles face detection in both ID photos and video frames.
Currently implemented as a stub, designed to be replaced with actual
face detection algorithms (e.g., MTCNN, DLib, or OpenCV cascades).
"""
def __init__(self, confidence_threshold: float = 0.8):
"""
Initialize the face detector.
Parameters
----------
confidence_threshold : float, optional
Minimum confidence threshold for face detection, by default 0.8
"""
self.confidence_threshold = confidence_threshold
logger.info(f"FaceDetector initialized with confidence_threshold={confidence_threshold}")
def detect_faces(self, image_path: str) -> List[Dict[str, Any]]:
"""
Detect faces in an image.
This is currently a stub implementation that simulates face detection.
In the future, this will be replaced with actual face detection algorithms.
Parameters
----------
image_path : str
Path to the image file
Returns
-------
List[Dict[str, Any]]
List of detected faces with bounding boxes and confidence scores
"""
logger.debug(f"Detecting faces in {image_path} (stub implementation)")
# Validate input file
if not os.path.exists(image_path):
logger.error(f"Image file not found: {image_path}")
raise FileNotFoundError(f"Image file not found: {image_path}")
# Stub implementation: simulate detecting one face
# In a real implementation, this would use actual face detection
detected_faces = [
{
"bbox": [100, 100, 200, 200], # x1, y1, x2, y2
"confidence": 0.95,
"landmarks": None, # Facial landmarks if available
"image_path": image_path
}
]
logger.debug(f"Detected {len(detected_faces)} faces")
return detected_faces
class EmbeddingExtractor:
"""
Facial embedding extraction component.
This class extracts facial feature embeddings from detected faces.
Currently implemented as a stub, designed to be replaced with actual
embedding extraction models (e.g., FaceNet, ArcFace, or VGGFace).
"""
def __init__(self, model_path: Optional[str] = None):
"""
Initialize the embedding extractor.
Parameters
----------
model_path : Optional[str], optional
Path to the embedding extraction model, by default None
"""
self.model_path = model_path
logger.info(f"EmbeddingExtractor initialized with model_path={model_path}")
def extract_embedding(self, image_path: str, face_bbox: List[int]) -> Optional[np.ndarray]:
"""
Extract facial embedding from a face region.
This is currently a stub implementation that returns a random embedding.
In the future, this will extract actual facial embeddings using deep learning models.
Parameters
----------
image_path : str
Path to the image file
face_bbox : List[int]
Bounding box coordinates [x1, y1, x2, y2]
Returns
-------
Optional[np.ndarray]
Facial embedding vector, or None if extraction fails
"""
logger.debug(f"Extracting embedding from {image_path} with bbox {face_bbox}")
# Validate input file
if not os.path.exists(image_path):
logger.error(f"Image file not found: {image_path}")
return None
# Stub implementation: return deterministic 128-dimensional embedding for testing
# In a real implementation, this would use a trained model
# Use a seed based on the image path to make it deterministic for testing
import hashlib
seed = int(hashlib.md5(image_path.encode()).hexdigest()[:8], 16) % 2**32
np.random.seed(seed)
embedding = np.random.randn(128).astype(np.float32)
# Normalize the embedding
embedding = embedding / np.linalg.norm(embedding)
logger.debug(f"Extracted embedding with shape {embedding.shape}")
return embedding
class SimilarityCalculator:
"""
Similarity calculation component for comparing facial embeddings.
This class computes similarity scores between facial embeddings using
various distance metrics. Currently supports cosine similarity.
"""
def __init__(self):
"""Initialize the similarity calculator."""
logger.info("SimilarityCalculator initialized")
def calculate_similarity(self, embedding1: np.ndarray, embedding2: np.ndarray) -> float:
"""
Calculate similarity between two facial embeddings.
Parameters
----------
embedding1 : np.ndarray
First facial embedding
embedding2 : np.ndarray
Second facial embedding
Returns
-------
float
Similarity score between 0.0 (dissimilar) and 1.0 (identical)
"""
# Calculate cosine similarity
dot_product = np.dot(embedding1, embedding2)
norm1 = np.linalg.norm(embedding1)
norm2 = np.linalg.norm(embedding2)
if norm1 == 0 or norm2 == 0:
return 0.0
cosine_similarity = dot_product / (norm1 * norm2)
# Convert to similarity score (0.0 to 1.0)
similarity = (cosine_similarity + 1.0) / 2.0
logger.debug(f"Calculated similarity: {similarity}")
return similarity
class FacialEmbeddingMatcher:
"""
Main facial embedding matcher for identity verification.
This class orchestrates the complete facial recognition pipeline:
face detection, embedding extraction, and similarity comparison.
It serves as the primary interface for facial matching functionality.
"""
def __init__(
self,
detector_confidence: float = 0.8,
similarity_threshold: float = 0.7,
embedding_model_path: Optional[str] = None
):
"""
Initialize the facial embedding matcher.
Parameters
----------
detector_confidence : float, optional
Confidence threshold for face detection, by default 0.8
similarity_threshold : float, optional
Similarity threshold for facial matching, by default 0.7
embedding_model_path : Optional[str], optional
Path to embedding extraction model, by default None
"""
self.detector_confidence = detector_confidence
self.similarity_threshold = similarity_threshold
self.embedding_model_path = embedding_model_path
# Initialize components
self.face_detector = FaceDetector(confidence_threshold=detector_confidence)
self.embedding_extractor = EmbeddingExtractor(model_path=embedding_model_path)
self.similarity_calculator = SimilarityCalculator()
logger.info(
"FacialEmbeddingMatcher initialized",
extra={
"detector_confidence": detector_confidence,
"similarity_threshold": similarity_threshold,
"embedding_model_path": embedding_model_path
}
)
def match_faces(
self,
id_image_path: str,
video_path: str,
frame_sample_rate: int = 10
) -> Dict[str, Any]:
"""
Match faces between ID image and video frames.
This method performs comprehensive facial matching by:
1. Detecting faces in the ID image
2. Sampling frames from the video and detecting faces
3. Extracting embeddings from detected faces
4. Computing similarity scores
5. Determining overall match result
Parameters
----------
id_image_path : str
Path to the ID document image
video_path : str
Path to the user video
frame_sample_rate : int, optional
Rate at which to sample video frames, by default 10
Returns
-------
Dict[str, Any]
Matching results with similarity scores and metadata
"""
logger.info(f"Starting facial matching between {id_image_path} and {video_path}")
try:
# Step 1: Extract reference embedding from ID image
id_faces = self.face_detector.detect_faces(id_image_path)
if not id_faces:
return {
"success": False,
"error": "No faces detected in ID image",
"similarity_score": 0.0,
"matches": False,
"details": {
"id_faces_detected": 0,
"video_faces_detected": 0,
"processing_timestamp": datetime.now(timezone.utc).isoformat()
}
}
except FileNotFoundError as e:
return {
"success": False,
"error": f"File not found: {str(e)}",
"similarity_score": 0.0,
"matches": False,
"details": {
"id_faces_detected": 0,
"video_faces_detected": 0,
"processing_timestamp": datetime.now(timezone.utc).isoformat()
}
}
# Extract embedding from the first (best) face in ID image
id_face = id_faces[0]
id_embedding = self.embedding_extractor.extract_embedding(
id_image_path, id_face["bbox"]
)
if id_embedding is None:
return {
"success": False,
"error": "Failed to extract embedding from ID image",
"similarity_score": 0.0,
"matches": False,
"details": {
"id_faces_detected": len(id_faces),
"video_faces_detected": 0,
"processing_timestamp": datetime.now(timezone.utc).isoformat()
}
}
# Step 2: Extract faces from video frames
video_faces = self._extract_faces_from_video(video_path, frame_sample_rate)
if not video_faces:
return {
"success": False,
"error": "No faces detected in video",
"similarity_score": 0.0,
"matches": False,
"details": {
"id_faces_detected": len(id_faces),
"video_faces_detected": 0,
"processing_timestamp": datetime.now(timezone.utc).isoformat()
}
}
# Step 3: Compare embeddings and find best match
best_similarity = 0.0
best_video_face = None
for video_face in video_faces:
video_embedding = self.embedding_extractor.extract_embedding(
video_path, video_face["bbox"]
)
if video_embedding is not None:
similarity = self.similarity_calculator.calculate_similarity(
id_embedding, video_embedding
)
if similarity > best_similarity:
best_similarity = similarity
best_video_face = video_face
# Step 4: Determine if faces match
matches = best_similarity >= self.similarity_threshold
result = {
"success": True,
"matches": matches,
"similarity_score": best_similarity,
"similarity_threshold": self.similarity_threshold,
"details": {
"id_faces_detected": len(id_faces),
"video_faces_detected": len(video_faces),
"best_video_face": best_video_face,
"processing_timestamp": datetime.now(timezone.utc).isoformat(),
"frame_sample_rate": frame_sample_rate,
"note": "This is a stub implementation. Real facial recognition will be implemented in the future."
}
}
logger.info(
"Facial matching completed",
extra={
"matches": matches,
"similarity_score": best_similarity,
"faces_detected_id": len(id_faces),
"faces_detected_video": len(video_faces)
}
)
return result
except Exception as e:
logger.error(f"Error during facial matching: {str(e)}", exc_info=True)
return {
"success": False,
"error": f"Processing error: {str(e)}",
"similarity_score": 0.0,
"matches": False,
"details": {
"processing_timestamp": datetime.now(timezone.utc).isoformat()
}
}
def _extract_faces_from_video(self, video_path: str, frame_sample_rate: int) -> List[Dict[str, Any]]:
"""
Extract faces from video frames.
This method samples frames from the video and detects faces in each frame.
Currently implemented as a stub that simulates face detection.
Parameters
----------
video_path : str
Path to the video file
frame_sample_rate : int
Rate at which to sample frames
Returns
-------
List[Dict[str, Any]]
List of detected faces with frame information
"""
logger.debug(f"Extracting faces from video: {video_path}")
# Stub implementation: simulate detecting faces in video
# In a real implementation, this would:
# 1. Open the video file
# 2. Sample frames at the specified rate
# 3. Detect faces in each sampled frame
# 4. Return face information with frame metadata
detected_faces = [
{
"bbox": [120, 120, 220, 220], # x1, y1, x2, y2
"confidence": 0.92,
"frame_number": 15,
"timestamp": 0.5, # seconds
"image_path": video_path
},
{
"bbox": [110, 110, 210, 210],
"confidence": 0.88,
"frame_number": 30,
"timestamp": 1.0,
"image_path": video_path
}
]
logger.debug(f"Extracted {len(detected_faces)} faces from video")
return detected_faces
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