README.md

metadata

language:
  - en
license: cc-by-4.0
size_categories:
  - 1M<n<10M
task_categories:
  - object-detection
  - feature-extraction
tags:
  - movies
  - tv-series
  - facial-recognition
  - computer-vision
  - embeddings
  - face-detection
  - imdb
pretty_name: CineFace Database

CineFace

CineFace is a comprehensive ecosystem for facial analysis in entertainment media. It consists of:

1. The CineFace Dataset: A massive collection of detections and embeddings from over 6,000 movies and TV series.
2. The CineFace Toolkit: Pipeline for large-scale facial detection, encoding, and identification in TV and Film.

📊 View Dashboard | 🤗 Hugging Face Dataset

Dataset

The CineFace database contains metadata and facial detections for over 6,000 titles. You can download the components directly from Hugging Face:

• Film List: film_list.csv — Comprehensive list of all movies and series in the DB.
• Detections: faces.tar.gz — Bounding boxes and identifications.
• Encodings: embeddings.tar.gz — Pre-computed face embeddings.
• Relational DB: CineFaceDW.db — SQLite version of the dataset.

Using the Encodings

The encodings are saved as .npz files. Since the encoded faces are stored in sequence, you can join them to the detection metadata by loading the corresponding CSV and adding the array as a column:

import numpy as np
import pandas as pd

# Load metadata and embeddings
df = pd.read_csv("movie_12345.csv")
embeddings = np.load("movie_12345.npz")['embeddings']

# Join (sequence based)
df['encoding'] = list(embeddings)

Toolkit (Installation and Usage)

Requirements

CineFace relies on Docker and Qdrant. To install Qdrant, just run with Docker. It will download the image automatically

docker run -p 6333:6333 qdrant/qdrant

Install

Simply download the source code

git clone https://github.com/astaileyyoung/CineFace.git

Then install the required dependencies

pip install -r requirements.txt

Finally, install CineFace

pip install -e .

CineFace uses Visage as a backend for accurate, high-performance facial detection and encoding. Visage can also be used independently.

**Be advised that the associated docker image is quite large (17GB) since it relies on heavy ML libraries built from source, so it will take a while to download (10-15 minutes).

Usage

Running CineFace is straightforward.

Basic Command

cineface <src> <dst> [options]

• <src>: Path to the input video file
• <dst>: Path to the output file

Command-Line Arguments

Argument	Type	Default	Description
src	str	(required)	Path to input video file or directory.
dst	str	(required)	Path to output directory or results file.
imdb_id	int	(required)	IMDb ID (just the numbers).
--faces_dir	str	None	Directory to save face images to
--encoding_col	str	'embedding'	Column name for face embeddings.
--image	str	'astaileyyoung/visage'	Container/image name (for debugging/development).
--frameskip	int	24	Number of frames to skip between detections.
--threshold, -t	float	0.5	Recognition confidence threshold.
--timeout	int	60	Timeout (in seconds) for matching.
--batch_size	int	256	Batch size for matching.
--season	int	None	Season number (required for matching tv show).
--episode	int	None	Episode number (requird for matching tv show).
--qdrant_client	str	'localhost'	Qdrant client address (vector DB).
--qdrant_port	int	6333	Qdrant port.

**Automatic tv/movie identification by filename is no longer working due to change in the IMDb API that has broken Cinemagoer search, which automatic identification depends on. If analyzing a movie, you must enter the imdb_id. If analyzing a TV show, you must enter the imdb_id, season, and episode.

Research and Analysis

Notebooks analyzing the dataset can be found in CineFace/notebooks/research. Feel free to submit a ticket if you encounter bugs or have feature requests for the dashboard.