Parameters for the Face Identity Extractor. The Face Identity Extractor processes images or video frames to detect, align, and embed faces using production-grade SOTA models (SCRFD + ArcFace). Core Pipeline: 1. SCRFD Detection → Bounding boxes + 5 landmarks 2. 5-Point Affine Alignment → 112×112 canonical face 3. ArcFace Embedding → 512-d L2-normalized vector 4. Optional Quality Scoring → Filter low-quality faces Use Cases: - Face verification (1:1 matching) - Face identification (1:N search) - Face clustering (group photos by person) - Duplicate face detection
| Name | Type | Description | Notes |
|---|---|---|---|
| extractor_type | str | Discriminator field for parameter type identification. Must be 'face_identity_extractor'. | [optional] [default to 'face_identity_extractor'] |
| detection_model | str | SCRFD model for face detection. 'scrfd_500m': Fastest (2-3ms). 'scrfd_2.5g': Balanced (5-7ms), recommended. 'scrfd_10g': Highest accuracy (10-15ms). | [optional] [default to 'scrfd_2.5g'] |
| min_face_size | int | Minimum face size in pixels to detect. 20px: Balanced. 40px: Higher quality. 10px: Maximum recall. | [optional] [default to 20] |
| detection_threshold | float | Confidence threshold for face detection (0.0-1.0). | [optional] [default to 0.5] |
| max_faces_per_image | int | Maximum number of faces to process per image. None: Process all. | [optional] |
| normalize_embeddings | bool | L2-normalize embeddings to unit vectors (recommended). | [optional] [default to True] |
| enable_quality_scoring | bool | Compute quality scores (blur, size, landmarks). Adds ~5ms per face. | [optional] [default to True] |
| quality_threshold | float | Minimum quality score to index faces. None: Index all faces. 0.5: Moderate filtering. 0.7: High quality only. | [optional] |
| max_video_length | int | Maximum video length in seconds. 60: Default. 10: Recommended for retrieval. 300: Maximum (extraction only). | [optional] [default to 60] |
| video_sampling_fps | float | Frames per second to sample from video. 1.0: One frame per second (recommended). | [optional] [default to 1] |
| video_deduplication | bool | Remove duplicate faces across video frames (extraction only). Reduces 90-95% redundancy. NOT used in retrieval. | [optional] [default to True] |
| video_deduplication_threshold | float | Cosine similarity threshold for deduplication. 0.8: Conservative (default). | [optional] [default to 0.8] |
| output_mode | str | 'per_face': One document per face (recommended). 'per_image': One doc per image with faces array. | [optional] [default to 'per_face'] |
| include_face_crops | bool | Include aligned 112×112 face crops as base64. Adds ~5KB per face. | [optional] [default to False] |
| include_source_frame_thumbnail | bool | Include resized source frame/image as base64 thumbnail (~15-30KB per face). Used for display with bounding box overlay. | [optional] [default to False] |
| store_detection_metadata | bool | Store bbox, landmarks, detection scores. Recommended for debugging. | [optional] [default to True] |
from mixpeek.models.face_identity_extractor_params import FaceIdentityExtractorParams
# TODO update the JSON string below
json = "{}"
# create an instance of FaceIdentityExtractorParams from a JSON string
face_identity_extractor_params_instance = FaceIdentityExtractorParams.from_json(json)
# print the JSON string representation of the object
print(FaceIdentityExtractorParams.to_json())
# convert the object into a dict
face_identity_extractor_params_dict = face_identity_extractor_params_instance.to_dict()
# create an instance of FaceIdentityExtractorParams from a dict
face_identity_extractor_params_from_dict = FaceIdentityExtractorParams.from_dict(face_identity_extractor_params_dict)