Enable Face and Face Attribute Recognition

FindFace Multi allows you to recognize human faces and face attributes. Subject to your needs, you can enable recognition of such face attributes as age, gender, emotions, beard, glasses, medical masks, head position, eyes state, or liveness.

Face and face attribute recognition can be automatically enabled and configured during the FindFace Multi installation. If you skipped this step, you can manually do it later. Face and face attribute recognition works on both GPU- and CPU-acceleration.

Face object recognition is enabled by default. In case you removed face as a recognition object during the installation, you can add it later by following the below steps. If face object recognition is already installed, and you only need to enable face attribute recognition, jump to the steps 1.5, 4.1, 4.2. Other steps should be skipped.

  1. To enable face recognition, do the following:

    Specify neural network models for face object detection in the /opt/findface-multi/configs/findface-extraction-api/findface-extraction-api.yaml configuration file.

    Important

    Be sure to choose the right acceleration type for each model, matching the acceleration type of findface-extraction-api: CPU or GPU. Be aware that findface-extraction-api on CPU can work only with CPU-models, while findface-extraction-api on GPU supports both CPU- and GPU-models.

    1. Open the findface-extraction-api.yaml configuration file.

      sudo vi /opt/findface-multi/configs/findface-extraction-api/findface-extraction-api.yaml

    2. Specify the face detector model in the detectors → models section by pasting the following code:

      GPU

      detectors:
  ...
    models:
      face_jasmine:
        aliases:
        - face
        - nnd
        - cheetah
        model: detector/facedet.kali.005.gpu.fnk
        options:
          min_object_size: 32
          resolutions:
          - 2048x2048
  ...

      CPU

      detectors:
  ...
    models:
      face_jasmine:
        aliases:
        - face
        - nnd
        - cheetah
        model: detector/facedet.kali.005.cpu.fnk
        options:
          min_object_size: 32
          resolutions:
          - 2048x2048
  ...

    3. Make sure that the objects → face section contains the quality_attribute: face_quality and the base_normalizer: facenorm/crop2x.v2_maxsize400.gpu.fnk or the base_normalizer: facenorm/crop2x.v2_maxsize400.cpu.fnk, depending on your acceleration type:

      GPU

      objects:
  ...
  face:
    base_normalizer: facenorm/crop2x.v2_maxsize400.gpu.fnk
    quality_attribute: face_quality
  ...

      CPU

      objects:
  ...
  face:
    base_normalizer: facenorm/crop2x.v2_maxsize400.cpu.fnk
    quality_attribute: face_quality
  ...

    4. Specify the face normalizer models in the normalizers section by pasting the following code:

      GPU

      normalizers:
  ...
  models:
    crop1x:
      model: facenorm/crop1x.v2_maxsize400.gpu.fnk
    crop2x:
      model: facenorm/crop2x.v2_maxsize400.gpu.fnk
    cropbbox:
      model: facenorm/cropbbox.v2.gpu.fnk
    multicrop_full_center:
      model: ''
    multicrop_full_crop2x:
      model: facenorm/facenorm.multicrop_full_crop2x_size400.gpu.fnk
    norm200:
      model: facenorm/bee.v3.gpu.fnk
  ...

      CPU

      normalizers:
  ...
  models:
    crop1x:
      model: facenorm/crop1x.v2_maxsize400.cpu.fnk
    crop2x:
      model: facenorm/crop2x.v2_maxsize400.cpu.fnk
    cropbbox:
      model: facenorm/cropbbox.v2.cpu.fnk
    multicrop_full_center:
      model: ''
    multicrop_full_crop2x:
      model: facenorm/facenorm.multicrop_full_crop2x_size400.cpu.fnk
    norm200:
      model: facenorm/bee.v3.cpu.fnk
  ...

    5. Note

      This step is required to enable face attribute recognition.

      To enable face attribute recognition, do the following:

      In the /opt/findface-multi/configs/findface-extraction-api/findface-extraction-api.yaml configuration file, specify the extraction models in the extractors section, as shown in the example below. Be sure to indicate the right acceleration type for each model, matching the acceleration type of findface-extraction-api: CPU or GPU.

      GPU

      extractors:
  ...
  models:
    face_age:
      default:
        model: faceattr/faceattr.age.v3.gpu.fnk
    face_beard:
      default:
        model: faceattr/beard.v0.gpu.fnk
    face_beard4:
      default:
        model: ''
    face_countries47:
      default:
        model: ''
    face_emben:
      default:
        model: face/nectarine_l_320.gpu.fnk
    face_emotions:
      default:
        model: faceattr/emotions.v1.gpu.fnk
    face_eyes_attrs:
      default:
        model: faceattr/faceattr.eyes_attrs.v0.gpu.fnk
    face_eyes_openness:
      default:
        model: ''
    face_gender:
      default:
        model: faceattr/faceattr.gender.v3.gpu.fnk
    face_glasses3:
      default:
        model: ''
    face_glasses4:
      default:
        model: faceattr/faceattr.glasses4.v0.gpu.fnk
    face_hair:
      default:
        model: ''
    face_headpose:
      default:
        model: faceattr/headpose.v3.gpu.fnk
    face_headwear:
      default:
        model: ''
    face_highlight:
      default:
        model: ''
    face_liveness:
      default:
        model: faceattr/faceattr.liveness_web.v1.gpu.fnk
    face_luminance_overexposure:
      default:
        model: ''
    face_luminance_underexposure:
      default:
        model: ''
    face_luminance_uniformity:
      default:
        model: ''
    face_medmask3:
      default:
        model: faceattr/medmask3.v2.gpu.fnk
    face_medmask4:
      default:
        model: ''
    face_mouth_attrs:
      default:
        model: ''
    face_quality:
      default:
        model: faceattr/faceattr.quality.v5.gpu.fnk
    face_scar:
      default:
        model: ''
    face_sharpness:
      default:
        model: ''
    face_tattoo:
      default:
        model: ''
    face_validity:
      default:
        model: ''

      CPU

      extractors:
  ...
  models:
    face_age:
      default:
        model: faceattr/faceattr.age.v3.cpu.fnk
    face_beard:
      default:
        model: faceattr/beard.v0.cpu.fnk
    face_beard4:
      default:
        model: ''
    face_countries47:
      default:
        model: ''
    face_emben:
      default:
        model: face/nectarine_l_320.cpu.fnk
    face_emotions:
      default:
        model: faceattr/emotions.v1.cpu.fnk
    face_eyes_attrs:
      default:
        model: faceattr/faceattr.eyes_attrs.v0.cpu.fnk
    face_eyes_openness:
      default:
        model: ''
    face_gender:
      default:
        model: faceattr/faceattr.gender.v3.cpu.fnk
    face_glasses3:
      default:
        model: ''
    face_glasses4:
      default:
        model: faceattr/faceattr.glasses4.v0.cpu.fnk
    face_hair:
      default:
        model: ''
    face_headpose:
      default:
        model: faceattr/headpose.v3.cpu.fnk
    face_headwear:
      default:
        model: ''
    face_highlight:
      default:
        model: ''
    face_liveness:
      default:
        model: faceattr/faceattr.liveness_web.v1.cpu.fnk
    face_luminance_overexposure:
      default:
        model: ''
    face_luminance_underexposure:
      default:
        model: ''
    face_luminance_uniformity:
      default:
        model: ''
    face_medmask3:
      default:
        model: faceattr/medmask3.v2.cpu.fnk
    face_medmask4:
      default:
        model: ''
    face_mouth_attrs:
      default:
        model: ''
    face_quality:
      default:
        model: faceattr/faceattr.quality.v5.cpu.fnk
    face_scar:
      default:
        model: ''
    face_sharpness:
      default:
        model: ''
    face_tattoo:
      default:
        model: ''
    face_validity:
      default:
        model: ''

      The following extraction models are available:

      Extractor

      Acceleration

      Configure as follows

      age

      CPU

      face_age: faceattr/faceattr.age.v3.cpu.fnk

      GPU

      face_age: faceattr/faceattr.age.v3.gpu.fnk

      beard

      CPU

      face_beard: faceattr/beard.v0.cpu.fnk

      GPU

      face_beard: faceattr/beard.v0.gpu.fnk

      individual face feature vector

      CPU

      face_emben: face/nectarine_l_320.cpu.fnk

      GPU

      face_emben: face/nectarine_l_320.gpu.fnk

      gender

      CPU

      face_gender: faceattr/faceattr.gender.v3.cpu.fnk

      GPU

      face_gender: faceattr/faceattr.gender.v3.gpu.fnk

      emotions

      CPU

      face_emotions: faceattr/emotions.v1.cpu.fnk

      GPU

      face_emotions: faceattr/emotions.v1.gpu.fnk

      glasses

      CPU

      face_glasses3: faceattr/faceattr.glasses4.v0.cpu.fnk

      GPU

      face_glasses3: faceattr/faceattr.glasses4.v0.gpu.fnk

      head position

      CPU

      face_headpose: faceattr/headpose.v3.cpu.fnk

      GPU

      face_headpose: faceattr/headpose.v3.gpu.fnk

      face liveness

      CPU

      face_liveness: faceattr/faceattr.liveness_web.v1.cpu.fnk

      GPU

      face_liveness: faceattr/faceattr.liveness_web.v1.gpu.fnk

      face mask

      CPU

      face_medmask3: faceattr/medmask3.v2.cpu.fnk

      GPU

      face_medmask3: faceattr/medmask3.v2.gpu.fnk

      face quality

      CPU

      face_quality: faceattr/faceattr.quality.v5.cpu.fnk

      GPU

      face_quality: faceattr/faceattr.quality.v5.gpu.fnk

      eyes

      CPU

      face_eyes_attrs: faceattr/faceattr.eyes_attrs.v0.cpu.fnk

      GPU

      face_eyes_attrs: faceattr/faceattr.eyes_attrs.v0.gpu.fnk

      Important

      The face_liveness extraction model liveness.web.v1 is enabled by default. Do not disable it if you use authentication by face.

  2. To enable face recognition, modify the /opt/findface-multi/configs/findface-video-worker/findface-video-worker.yaml configuration file.

    1. In the models section, specify the face neural network models by analogy with the example below:

      GPU

      sudo vi /opt/findface-multi/configs/findface-video-worker/findface-video-worker.yaml

models:
  ...
  detectors:
    ...
    face:
      fnk_path: /usr/share/findface-data/models/detector/facedet.kali.005.gpu.fnk
      min_size: 60
    ...
  normalizers:
    ...
    face_norm:
      fnk_path: /usr/share/findface-data/models/facenorm/crop2x.v2_maxsize400.gpu.fnk
    face_norm_quality:
      fnk_path: /usr/share/findface-data/models/facenorm/crop1x.v2_maxsize400.gpu.fnk
    ...
  extractors:
    ...
    face_quality:
      fnk_path: /usr/share/findface-data/models/faceattr/faceattr.quality.v5.gpu.fnk
      normalizer: face_norm_quality

      CPU

      sudo vi /opt/findface-multi/configs/findface-video-worker/findface-video-worker.yaml

models:
  ...
  detectors:
    ...
    face:
      fnk_path: /usr/share/findface-data/models/detector/facedet.kali.005.cpu.fnk
      min_size: 60
    ...
  normalizers:
    ...
    face_norm:
      fnk_path: /usr/share/findface-data/models/facenorm/crop2x.v2_maxsize400.cpu.fnk
    face_norm_quality:
      fnk_path: /usr/share/findface-data/models/facenorm/crop1x.v2_maxsize400.cpu.fnk
    ...
  extractors:
    ...
    face_quality:
      fnk_path: /usr/share/findface-data/models/faceattr/faceattr.quality.v5.cpu.fnk
      normalizer: face_norm_quality

    2. Make sure that the objects → face section is included:

      objects:
  ...
  face:
    normalizer: face_norm
    quality: face_quality
    track_features: ''

  3. To enable face recognition, open the /opt/findface-multi/configs/findface-video-manager/findface-video-manager.yaml configuration file and make sure it contains the face section in detectors that looks similar to the example below.

    sudo vi /opt/findface-multi/configs/findface-video-manager/findface-video-manager.yaml

detectors:
  face:
    filter_min_quality: 0.42
    filter_min_size: 60
    filter_max_size: 8192
    roi: ''
    fullframe_crop_rot: false
    fullframe_use_png: false
    jpeg_quality: 95
    overall_only: true
    realtime_post_first_immediately: false
    realtime_post_interval: 1
    realtime_post_every_interval: false
    track_interpolate_bboxes: true
    track_miss_interval: 1
    track_overlap_threshold: 0.25
    track_max_duration_frames: 0
    track_send_history: false
    post_best_track_frame: true
    post_best_track_normalize: true
    post_first_track_frame: false
    post_last_track_frame: false
    tracker_type: simple_iou
    track_deep_sort_matching_threshold: 0.65
    track_deep_sort_filter_unconfirmed_tracks: true
    track_object_is_principal: false
    track_history_active_track_miss_interval: 0
    filter_track_min_duration_frames: 1
    extractors_track_triggers: {}

  4. Note

    This step is required to enable face attribute recognition.

    Enable recognition of face attributes in the /opt/findface-multi/configs/findface-multi-legacy/findface-multi-legacy.py configuration file.

    1. In the FFSECURITY section, specify the face attributes that you want to display for the face recognition events.

      sudo vi /opt/findface-multi/configs/findface-multi-legacy/findface-multi-legacy.py

available features: age, beard, emotions, gender, glasses, headpose, medmask, eyes_attrs
'FACE_EVENTS_FEATURES': ['gender', 'beard', 'emotions', 'headpose', 'age', 'medmask', 'glasses', 'eyes_attrs'],

    2. Restart all FindFace Multi containers.

      cd /opt/findface-multi/

sudo docker-compose restart