###### tags: `image process`

# face recognition

---

https://machinelearningmastery.com/how-to-develop-a-face-recognition-system-using-facenet-in-keras-and-an-svm-classifier/

---

https://medium.com/data-science-lab-amsterdam/face-recognition-with-python-in-an-hour-or-two-d271324cbeb3

https://github.com/davidsandberg/facenet.

http://krasserm.github.io/2018/02/07/deep-face-recognition/

distanceInputFromDB =[]

for j in embedded:
distanceInputFromDB.append(distance([embedded[test_idx][example_idx], j)

If np.mean(distanceInputFromDB) < unknown_treshold:
example_prediction = svc.predict([embedded[test_idx][example_idx]])
example_identity = encoder.inverse_transform(example_prediction)[0]

plt.imshow(example_image)
plt.title(f'Recognized as {example_identity}');

else:
plt.imshow(example_image)
plt.title('Unknown person');

# Detect face and return bounding boxs
bbm = alignment.getAllFaceBoundingBoxes(jc_orig_m)

jc_aligned_m = []
for (i,rect) in enumerate(bbm):
(x,y,w,h)=rect_to_bb(rect)
print(i)
print(rect)
jc_aligned_m.append(alignment.align(96, jc_orig_m, rect, landmarkIndices=AlignDlib.OUTER_EYES_AND_NOSE))
clone = jc_orig_m.copy()
cv2.rectangle(clone, (x, y), (x+w, y+h), (0, 255, 0), 2)
startX = x
startY = y - 15 if y - 15 > 15 else y + 15
cv2.putText(clone, str(i), (startX, startY),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
roi = jc_orig_m[y:y + h, x:x + w]
plt.subplot(5,5,i+1)
plt.imshow(roi)
plt.imshow(clone)

https://towardsdatascience.com/how-to-build-a-face-detection-and-recognition-system-f5c2cdfbeb8c

ptyhon machine learn tool
Streamlit

https://www.freecodecamp.org/news/making-your-own-face-recognition-system-29a8e728107c/

---
___

https://github.com/richmondu/libfaceid
https://github.com/ksachdeva/face-embeddings-generator
https://github.com/vinayakkailas/Face_Recognition

---

insert iframe to hackermd method

<iframe width="100%" height="500" src="https://hackmd.io/features" frameborder="0"></iframe>