1from realutils.face.insightface import isf_face_batch_similarity, isf_analysis_faces, isf_faces_visualize
23image_path ="/your/image/file"4# get the analysis all the faces5faces = isf_analysis_faces(image_path)6print(faces)78# compare them9print(isf_face_batch_similarity([face.embedding for face in faces]))1011# visualize it12isf_faces_visualize(image_path, faces).show()13
Available Models
We evaluated all these models with some evaluation datasets on face recognition.
CFPW (500 ids/7K images/7K pairs)[1]
LFW (5749 ids/13233 images/6K pairs)[2]
CALFW (5749 ids/13233 images/6K pairs)[3]
CPLFW (5749 ids/13233 images/6K pairs)[4]
Below are the complete results and recommended thresholds.
Det: Success rate of face detection and landmark localization.
Rec-F1: Maximum F1 score achieved in face recognition.
Rec-Thresh: Optimal threshold determined by the maximum F1 score.
Model
Eval ALL (Det/Rec-F1/Rec-Thresh)
Eval CALFW (Det/Rec-F1/Rec-Thresh)
Eval CFPW (Det/Rec-F1/Rec-Thresh)
Eval CPLFW (Det/Rec-F1/Rec-Thresh)
Eval LFW (Det/Rec-F1/Rec-Thresh)
buffalo_l
99.88% / 98.34% / 0.2203
100.00% / 95.75% / 0.2273
99.99% / 99.66% / 0.1866
99.48% / 96.41% / 0.2207
100.00% / 99.85% / 0.2469
buffalo_s
99.49% / 96.87% / 0.1994
99.99% / 94.45% / 0.2124
99.65% / 98.64% / 0.1845
98.04% / 92.61% / 0.2019
100.00% / 99.68% / 0.2314
[1] Sengupta Soumyadip, Chen Jun-Cheng, Castillo Carlos, Patel Vishal M, Chellappa Rama, Jacobs David W, Frontal to profile face verification in the wild, WACV, 2016.
[2] Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller. Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments, 2007.
[3] Zheng Tianyue, Deng Weihong, Hu Jiani, Cross-age lfw: A database for studying cross-age face recognition in unconstrained environments, arXiv:1708.08197, 2017.
[4] Zheng, Tianyue, and Weihong Deng. Cross-Pose LFW: A Database for Studying Cross-Pose Face Recognition in Unconstrained Environments, 2018.
