Introduction

Face Recognition module consists of several parts: 1.datasets and samplers, 2. backbone model zoo, 3. our proposed methods for face recognition, 4. test protocols of evaluation results and model latency.

Recent News

2024.12: SlerpFace: Face Template Protection via Spherical Linear Interpolation accpted by AAAI2025.

2024.03: Privacy-Preserving Face Recognition Using Trainable Feature Subtraction accpted by CVPR2024.

2023.10: Privacy-Preserving Face Recognition Using Random Frequency Components accpted by ICCV2023.

2022.9: Privacy-Preserving Face Recognition with Learnable Privacy Budgets in Frequency Domain accpted by ECCV2022.

2022.9: DuetFace: Collaborative Privacy-Preserving Face Recognition via Channel Splitting in the Frequency Domain accepted by ACMMM2022.

2022.6: Evaluation-oriented knowledge distillation for deep face recognition accepted by CVPR2022.

2022.2: We released some pretrain models for human_face and cartoon_face.

2021.7: We released a inference example for linux_x86 based on TNN framework.

2021.5: Federated Face Recognition. [paper]

2021.3: Consistent Instance False Positive Improves Fairness in Face Recognition accepted by CVPR2021. [paper]

2021.3: Spherical Confidence Learning for Face Recognition accepted by CVPR2021. [paper]

2020.8: Improving Face Recognition from Hard Samples via Distribution Distillation Loss accepted by ECCV2020. [paper]

2020.3: Curricularface: adaptive curriculum learning loss for deep face recognition has been accepted by CVPR2020. [paper]

Getting Started

Train Data

The training dataset is organized in tfrecord format for efficiency. The raw data of all face images are saved in tfrecord files, and each dataset has a corresponding index file(each line includes tfrecord_name, trecord_index offset, label).

The Dataset class will parse the index file to gather image data and label for training. This form of dataset is convenient for reorganization in data cleaning(do not reproduce tfrecord, just reproduce the index file).

1. Convert raw image to tfrecords, generate a new data dir including some tfrecord files and a index_map file

python3 tools/img2tfrecord.py --help
usage: img2tfrecord.py [-h] --img_list IMG_LIST --pts_list PTS_LIST
                       --tfrecords_name TFRECORDS_NAME

imgs to tfrecord

optional arguments:
  -h, --help            show this help message and exit
  --img_list IMG_LIST   path to the image file (default: None)
  --pts_list PTS_LIST   path to 5p list (default: None)
  --tfrecords_name TFRECORDS_NAME
                        path to the output of tfrecords dir path (default:
                        TFR-MS1M)

2. Convert old index file(each line includes image path, label) to new index file

python3 tools/convert_new_index.py --help
usage: convert_new_index.py [-h] --old OLD --tfr_index TFR_INDEX --new NEW

convert training index file

optional arguments:
  -h, --help            show this help message and exit
  --old OLD             path to old training list (default: None)
  --tfr_index TFR_INDEX
                        path to tfrecord index file (default: None)
  --new NEW             path to new training list (default: None)

3. Decode the tfrecords to raw image

python3 tools/decode.py --help
usage: decode.py [-h] --tfrecords_dir TFRECORDS_DIR --output_dir OUTPUT_DIR
                 --limit LIMIT

decode tfrecord

optional arguments:
  -h, --help            show this help message and exit
  --tfrecords_dir TFRECORDS_DIR
                        path to the output of tfrecords dir path (default:
                        None)
  --output_dir OUTPUT_DIR
                        path to the output of decoded imgs (default: None)
  --limit LIMIT         limit num of decoded samples (default: 10)

Train

Modified the DATA_ROOT and INDEX_ROOT in train.yaml, DATA_ROOT is the parent dir for tfrecord dir, INDEX_ROOT is the parent dir for index file.

bash local_train.sh

Test

Detail implementations and steps see Test

Inference

Detail implementations see Deploy

Model

Human face recognition models

Backbone	IJBB (TPR@FAR=1e-4)	IJBC (TPR@FAR=1e-4)	Download Links
IR_50	95.84	97.16	Google Drive
IR_101	96.30	97.51	Google Drive

Cartoon face recognition models

Backbone	iCartoonFace Top1	Download Links
IR_SE_101	88.07	Google Drive

Benchmark

Evaluation Results

Backbone	Head	Data	LFW	CFP-FP	CPLFW	AGEDB	CALFW	IJBB (TPR@FAR=1e-4)	IJBC (TPR@FAR=1e-4)
IR_101	ArcFace	MS1Mv2	99.77	98.27	92.08	98.15	95.45	94.2	95.6
IR_101	CurricularFace	MS1Mv2	99.80	98.36	93.13	98.37	96.05	94.86	96.15
IR_18	ArcFace	MS1Mv2	99.65	94.89	89.80	97.23	95.60	90.06	92.39
IR_34	ArcFace	MS1Mv2	99.80	97.27	91.75	98.07	95.97	92.88	94.65
IR_50	ArcFace	MS1Mv2	99.80	97.63	92.50	97.92	96.05	93.45	95.16
MobileFaceNet	ArcFace	MS1Mv2	99.52	91.66	87.93	95.82	95.12	87.07	89.13
GhostNet_x1.3	ArcFace	MS1Mv2	99.65	94.20	89.87	96.95	95.58	89.61	91.96
EfficientNetB0	ArcFace	MS1Mv2	99.60	95.90	91.07	97.58	95.82	91.79	93.67
EfficientNetB1	ArcFace	MS1Mv2	99.60	96.39	91.75	97.65	95.73	92.43	94.43

Backbone model size & latency

The device and platform information see below:

	Device	Inference Framework
x86 cpu	Intel(R) Xeon(R) Platinum 8255C CPU @ 2.50GHz	Openvino
arm	Kirin 980	TNN

Test results for different backbones and different devices：

Backbone	Model Size(fp32)	X86 CPU	ARM
EfficientNetB0	16MB	26.29ms	32.09ms
EfficientNetB1	26MB	35.73ms	46.5ms
MobileFaceNet	4.7MB	7.63ms	15.61ms
GhostNet_x1.3	16MB	25.70ms	27.58ms
IR_18	92MB	57.34ms	94.58ms
IR_34	131MB	105.58ms	NA
IR_50	167MB	165.95ms	NA
IR_101	249MB	215.47ms	NA

Acknowledgement

This repo is modified and adapted on these great repositories, we thank these authors a lot for their greate efforts.

• cavaface.pytorch
• face.evoLVe.PyTorch
• insightface
• mobile-vision