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1. ç°å¢èšå®
ãŸããå¿ èŠãªPythonã©ã€ãã©ãªãã€ã³ã¹ããŒã«ããå¿ èŠããããŸãã
pip install opencv-python scikit-learn pycryptodome
OpenCV (cv2): ç»ååŠçãšé¡æ€åºçšã scikit-learn: æ©æ¢°åŠç¿ã¢ã«ãŽãªãºã çšïŒäŸïŒé¡èªèçšïŒã pycryptodome: çäœèªèšŒãã³ãã¬ãŒãã®æå·åãšå®å šãªä¿åçšã
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2. é¡èªèã¢ãžã¥ãŒã«
ãã®ã¢ãžã¥ãŒã«ã¯ã顿€åºãç¹åŸŽæœåºãããã³ãããã³ã°ãåŠçããŸãã
import cv2
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
import os
class FaceRecognizer:
def __init__(self, training_data_path="training_faces", n_neighbors=3):
self.training_data_path = training_data_path
self.n_neighbors = n_neighbors
self.face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
self.model = None
self.labels = []
self.face_embeddings = []
def load_training_data(self):
if not os.path.exists(self.training_data_path):
print(f"Training data path not found: {self.training_data_path}")
return False
for dir_name in os.listdir(self.training_data_path):
subject_path = os.path.join(self.training_data_path, dir_name)
if not os.path.isdir(subject_path):
continue
label = dir_name # Use directory name as the label
self.labels.append(label)
for filename in os.listdir(subject_path):
if not filename.endswith(".jpg") and not filename.endswith(".png"):
continue
image_path = os.path.join(subject_path, filename)
image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
if image is None:
print(f"Could not read image: {image_path}")
continue
faces = self.face_cascade.detectMultiScale(image, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
if len(faces) > 0:
(x, y, w, h) = faces[0]
face_roi = image[y:y+h, x:x+w]
face_resized = cv2.resize(face_roi, (100, 100)) # Standardize size
face_flattened = face_resized.flatten()
self.face_embeddings.append(face_flattened)
if not self.face_embeddings:
print("No face embeddings found. Ensure training images contain faces.")
return False
return True
def train_model(self):
if not self.load_training_data():
return False
# Create label mapping (string labels to numerical labels)
unique_labels = list(set(self.labels))
self.label_map = {label: i for i, label in enumerate(unique_labels)}
numerical_labels = [self.label_map[label] for label in self.labels]
self.model = KNeighborsClassifier(n_neighbors=self.n_neighbors)
self.model.fit(self.face_embeddings, numerical_labels)
return True
def recognize_face(self, image):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = self.face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
if len(faces) == 0:
return None # No face detected
(x, y, w, h) = faces[0]
face_roi = gray[y:y+h, x:x+w]
face_resized = cv2.resize(face_roi, (100, 100))
face_flattened = face_resized.flatten()
if self.model is None:
print("Model not trained. Train the model first.")
return None
numerical_prediction = self.model.predict([face_flattened])[0]
# Reverse the label mapping to get the string label
predicted_label = next((label for label, i in self.label_map.items() if i == numerical_prediction), None)
return predicted_label
ãã®ã³ãŒãã¹ããããã¯ã以äžã®æ©èœãåããFaceRecognizerã¯ã©ã¹ãå®çŸ©ããŸãã
- æå®ããããã£ã¬ã¯ããªãããã¬ãŒãã³ã°ç»åãããŒãããŸãããã£ã¬ã¯ããªã¯ããããããç°ãªã人ç©ã衚ããµããã£ã¬ã¯ããªã§æ§æãããŠããå¿ èŠããããŸãããµããã£ã¬ã¯ããªã®ååã¯ããã®äººç©ã®ã©ãã«ãšããŠäœ¿çšãããŸãã
- OpenCVã®Haarã«ã¹ã±ãŒãåé¡åšã䜿çšããŠããã¬ãŒãã³ã°ç»åå ã®é¡ãæ€åºããŸãã
- æ€åºãããé¡ããç¹åŸŽãæœåºããŸãããã®ç°¡ç¥åãããäŸã§ã¯ãé¡é åã®ãµã€ãºã100x100ãã¯ã»ã«ã«å€æŽãã1Dé åã«å¹³åŠåããŸããããåªãã粟床ã®ããã«ã¯ãããæŽç·Žãããç¹åŸŽæœåºæè¡ïŒäŸïŒãã£ãŒãã©ãŒãã³ã°ã¢ãã«ã®äœ¿çšïŒã䜿çšã§ããŸãã
- æœåºãããç¹åŸŽã䜿çšããŠkè¿åæ³ïŒk-NNïŒåé¡åšããã¬ãŒãã³ã°ããŸãã
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3. æçŽã¹ãã£ã³ã¢ãžã¥ãŒã«
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- æçŽç¹åŸŽã®æœåº: æçŽç»åããç¹åŸŽãæœåºããŸããäžè¬çãªæçŽç¹åŸŽã«ã¯ã现éšç¹åŸŽç¹ïŒéç·ã®çµç«¯ãšåå²ç¹ïŒãå«ãŸããŸããSDKã¯ãããã®ç¹åŸŽãèªåçã«æœåºããæ©èœãæäŸããå ŽåããããŸãããããã¯ãNISTã®MINDTCTã®ãããªãªãŒãã³ãœãŒã¹ã©ã€ãã©ãªã䜿çšããããšãã§ããŸãã
- æçŽãã³ãã¬ãŒãã®ä¿å: æœåºãããæçŽç¹åŸŽããã³ãã¬ãŒããšããŠä¿åããŸãããã³ãã¬ãŒãã¯ãçæ³çã«ã¯æå·åããŠå®å šã«ä¿åããããšãéèŠã§ãã
- æçŽã®ãããã³ã°: ãŠãŒã¶ãŒãèªèšŒããéã«ãæ°ããæçŽç»åãååŸããç¹åŸŽãæœåºããããããä¿åããããã³ãã¬ãŒããšæ¯èŒããŸããSDKã¯ããã®ãããã³ã°ãå®è¡ããæ©èœãæäŸããå ŽåããããŸããçµæã¯éåžžã2ã€ã®æçŽéã®é¡äŒŒæ§ã瀺ãã¹ã³ã¢ã«ãªããŸãã
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4. ãã«ãã¢ãŒãã«èªèšŒããžãã¯
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# This is a simplified example. In a real-world scenario, you would use more robust error handling and security measures.
def authenticate_user(image, fingerprint_template, face_recognizer, fingerprint_scanner):
# Face Recognition
face_label = face_recognizer.recognize_face(image)
# Fingerprint Verification
fingerprint_match_score = fingerprint_scanner.verify_fingerprint(fingerprint_template)
# Decision Logic (Fusion)
# Here, we use a simple AND rule: both face and fingerprint must match for successful authentication.
# More sophisticated fusion methods can be used, such as weighted averaging or machine learning classifiers.
face_threshold = 0.7 # Example threshold. Adjust based on performance.
fingerprint_threshold = 0.8 # Example threshold. Adjust based on performance.
if face_label is not None and fingerprint_match_score >= fingerprint_threshold:
return face_label # Assuming face_label is the username or ID
else:
return None # Authentication failed
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- NIST MINUTIAE INTEROPERABILITY EXCHANGE TEST (MINDTCT): https://www.nist.gov/itl/iad/image-group/products-and-services/biometric-image-software/mindtct