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- Face_recognition: Dlibäžã«æ§ç¯ããããŠãŒã¶ãŒãã¬ã³ããªãŒãªã©ã€ãã©ãªã§ã顿€åºãšèªèã®ããã»ã¹ãç°¡çŽ åããŸãã
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1. ããŒã¿ååŸãšååŠç
ããã¯æåã«ããŠéèŠãªã¹ãããã§ãããŠãŒã¶ãŒããçäœèªèšŒãµã³ãã«ãååŸããŸãããã«ãã¢ãŒãã«ã·ã¹ãã ã®å Žåãç°ãªãã»ã³ãµãŒïŒé¡çšã«ã¡ã©ãæçŽã¹ãã£ããŒããã€ã¯ïŒããã®ããŒã¿ãåéãããŸãã
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- æçŽããŒã¿: æçŽã¹ãã£ããŒããã®ç»åãååŠçã«ã¯ãç»å匷調ããã€ãºäœæžãç¹åŸŽç¹ïŒminutiaeïŒæœåºãå«ãŸããŸãã
- é³å£°ããŒã¿: é³å£°é²é³ãååŠçã«ã¯ããã€ãºé€å»ãé³å£°æŽ»åæ€åºãç¹åŸŽæœåºïŒäŸïŒã¡ã«åšæ³¢æ°ã±ãã¹ãã©ã ä¿æ° - MFCCsïŒãå«ãŸããŸãã
- è¹åœ©ããŒã¿: ç¹æ®ãªè¹åœ©ã¹ãã£ããŒããã®ç»åãååŠçã«ã¯ãç³åã»ã°ã¡ã³ããŒã·ã§ã³ãè¹åœ©ã®å±æåãæ£èŠåãå«ãŸããŸãã
Pythonå®è£ äŸïŒOpenCVã«ãã顿€åºïŒ:
import cv2
# Load the pre-trained Haar cascade classifier for face detection
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
# Capture video from the default camera
cam = cv2.VideoCapture(0)
while True:
ret, frame = cam.read()
if not ret:
break
# Convert the frame to grayscale for Haar cascade to work efficiently
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Detect faces in the grayscale frame
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
# Draw rectangles around the detected faces
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
# Display the resulting frame
cv2.imshow('Face Detection', frame)
# Break the loop if 'q' is pressed
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release the camera and close all windows
cam.release()
cv2.destroyAllWindows()
2. ç¹åŸŽæœåº
çäœèªèšŒããŒã¿ãååŠçãããåŸãå人ãç¬èªã«èå¥ããé¢é£ããç¹åŸŽãæœåºãããŸããããã§æ©æ¢°åŠç¿ãšãã£ãŒãã©ãŒãã³ã°ãéèŠãªåœ¹å²ãæãããŸãã
- é¡ã®ç¹åŸŽ: é¡ã®ã©ã³ãããŒã¯ïŒç®ã錻ãå£ïŒéã®è·é¢ããã¯ã¹ãã£ãã¿ãŒã³ãããã³ç³ã¿èŸŒã¿ãã¥ãŒã©ã«ãããã¯ãŒã¯ïŒCNNïŒã«ãã£ãŠçæããããã£ãŒãã©ãŒãã³ã°åã蟌ã¿ã
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- é³å£°ã®ç¹åŸŽ: 声éã®ç¹æ§ãããããææãã¿ãŒã³ïŒMFCCsãŸãã¯ãã£ãŒãã©ãŒãã³ã°ã¢ãã«ã«ãã£ãŠè¡šçŸïŒã
- è¹åœ©ã®ç¹åŸŽ: ã¬ããŒã«ãã£ã«ã¿ãŒãŸãã¯ãã£ãŒãã©ãŒãã³ã°ç¹åŸŽã䜿çšããŠãšã³ã³ãŒãããããã¯ã¹ãã£ãã¿ãŒã³ã
Pythonå®è£ äŸïŒFace_recognitionã«ããé¡ç¹åŸŽæœåºïŒ:
import face_recognition
from PIL import Image
# Load an image of a person
known_image = face_recognition.load_image_file("person_a.jpg")
# Find all face locations and encodings in the image
face_locations = face_recognition.face_locations(known_image)
face_encodings = face_recognition.face_encodings(known_image, face_locations)
# Assuming only one face in the image, get the first encoding
if face_encodings:
known_face_encoding = face_encodings[0]
print("Facial encoding extracted:", known_face_encoding)
else:
print("No faces found in the image.")
# You can then store this 'known_face_encoding' along with a user ID for later comparison.
3. ãã³ãã¬ãŒãã®äœæãšä¿å
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