FREE Face Recognition Tools for Identity Verification

# Installation
pip install face_recognition

# Basic usage
import face_recognition
known_image = face_recognition.load_image_file("known.jpg")
unknown_image = face_recognition.load_image_file("unknown.jpg")

known_encoding = face_recognition.face_encodings(known_image)[0]
unknown_encoding = face_recognition.face_encodings(unknown_image)[0]

results = face_recognition.compare_faces([known_encoding], unknown_encoding)
print("Match found!" if results[0] else "No match.")

# Installation
pip install opencv-python

# Face detection example
import cv2
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

img = cv2.imread('test.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.1, 4)

for (x, y, w, h) in faces:
    cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)

cv2.imshow('Detected Faces', img)
cv2.waitKey(0)

# Installation
pip install deepface

# Analyze facial attributes
from deepface import DeepFace
obj = DeepFace.analyze(img_path = "img.jpg", 
        actions = ['age', 'gender', 'emotion', 'race'])
print(obj)

# Sample implementation using Keras
from keras.models import load_model
from keras.preprocessing import image
import numpy as np

model = load_model('facenet_keras.h5')
img = image.load_img('face.jpg', target_size=(160, 160))
img = image.img_to_array(img)
img = np.expand_dims(img, axis=0)
embedding = model.predict(img)

# Quick start with Docker
docker pull exadel/compreface
docker run -p 8000:8000 -it exadel/compreface

# Then access API at:
http://localhost:8000/docs