TFace: A trusty face recognition research platform developed by Tencent Youtu Lab. It provides a high-performance distributed training framework and releases our efficient methods implementation.

This framework consists of several modules: 1. various data augmentation methods, 2. backbone model zoo, 3. our proposed methods for face recognition and face quality, 4. test protocols of evalution results and model latency.


Recent News

2021.3: SDD-FIQA: Unsupervised Face Image Quality Assessment with Similarity Distribution Distance accepted by CVPR2021. [paper] [code]

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

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

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

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


  • python==3.6.0
  • torch==1.6.0
  • torchvision==0.7.0
  • tensorboard==2.4.0
  • Pillow==5.0.0

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 IndexTFRDataset 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/ --img_list=${img_list} --pts_list=${pts_list} --tfrecords_name=${tfr_data_name}

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

    python3 tools/ --old=${old_index} --tfr_index=${tfr_index} --new=${new_index}

  3. Decode the tfrecords to raw image

    python3 tools/ --tfrecords_dir=${tfr_dir} --output_dir=${output_dir}


Data Augmentation module implements some 2D-based methods to generated some hard samples, e.g., maks, glass, headscarf. Details see Augmentation


Modified the DATA_ROOTandINDEX_ROOTin ./tasks/distfc/train_confing.yaml, DATA_ROOT is the parent dir for tfrecord dir, INDEX_ROOT is the parent dir for index file.



Detail codes and steps see Test


Evaluation Results

Backbone Head Data LFW CFP-FP CPLFW AGEDB CALFW IJBB ([email protected]=1e-4) IJBC ([email protected]=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´╝Ü

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