UNPG

Unified Negative Pair Generation toward Well-discriminative Feature Space for Face Recognition

Data prepration

MS1MV2

MS1M-ArcFace (85K ids/5.8M images) download link

#Preprocess 'train.rec' and 'train.idx' to 'jpg'# examplecd detection
python rec2image.py --include '{path}/face_emore' --output '{path}/MS1MV2'

K-FACE

K-FACE download link

""" ################################################################### K-Face : Korean Facial Image AI Dataset url : http://www.aihub.or.kr/aidata/73 Directory structure : High-ID-Accessories-Lux-Emotion ID example : '19062421' ... '19101513' len 400 Accessories example : 'S001', 'S002' .. 'S006' len 6 Lux example : 'L1', 'L2' .. 'L30' len 30 Emotion example : 'E01', 'E02', 'E03' len 3 ###################################################################"""# examplecd detection
python align_kfaces.py --ori_data_path '/data/FACE/KFACE/High' --detected_data_path 'kface_retina_align_112x112'

IJBB & IJBC

download link

Please apply for permissions from NIST before your usage.

Evaluation

Performance on public benchmark datasets with ResNet-100

Method	IJB-B (1e-5)	IJB-B (1e-4)	IJB-C (1e-5)	IJB-C (1e-4)	MegaFace (Rank-1 acc)	LFW	Path
Circle-loss*	-	-	-	93.95	98.50	99.73	-
ArcFace*	-	94.20	-	95.60	98.35	99.82	-
MagFace*	90.36	94.51	94.08	95.97	-	99.83	-
CosFace	89.38	94.39	94.42	96.35	99.08	99.83	-
CosFace+UNPG	90.61	94.99	94.48	96.39	99.27	99.81	link
ArcFace	89.99	94.89	93.93	96.25	98.56	99.83	-
ArcFace+UNPG	90.57	95.04	94.47	96.33	98.82	99.83	link
MagFace	89.03	93.99	93.30	95.54	98.51	99.81	-
MagFace+UNPG	90.93	95.21	94.70	96.38	98.03	99.81	link

“*” indicates results from the original paper.

Example script

cd recognition
# example
python evaluation.py --weights 'face.r100.cos.unpg.wisk1.5.pt' --data 'ijbc.yaml'# --data (e.g., 'ijbb.yaml', 'bins.yaml')

Performance on K-FACE test datasets (Q1-Q4) with ResNet-34

Method	Q4 (1e-5)	Q4 (1e-4)	Q3 (1e-5)	Q3 (1e-4)	Q2 (1e-5)	Q2 (1e-4)	Q1 (1e-3)	Q1 (1e-2)	Path
ArcFace	0.05	0.29	2.06	4.40	26.56	41.29	94.00	100	-
SN-pair	3.50	7.21	17.67	21.16	21.93	33.26	91.80	97.60	-
MS-loss	5.68	8.70	15.15	18.74	38.33	46.64	94.60	99.20	-
MixFace	7.11	10.92	9.19	22.55	39.09	44.48	97.00	100	-
Circle-loss	18.08	25.05	33.56	41.54	71.38	77.93	100	100	-
Arc+UNPG	29.89	50.43	51.59	60.88	91.28	93.26	100	100	link

cd recognition
# example
python evaluation.py --weights 'kface.r34.arc.unpg.wisk1.0.pt' --data 'kface.yaml'

Training

Example script (FACE)

cd recognition
# example 
python train.py --model 'iresnet-100' --head 'arcface' --aux 'unpg' --data 'data/face.yaml' --hyp 'data/hyp.yaml' --name 'example' --device 0,1

Example script (KFACE)

cd recognition
# example 
python train.py --model 'iresnet-34' --head 'arcface' --aux 'unpg' --data 'data/kface.yaml' --hyp 'data/hyp.yaml' --name 'example' --device 0,1