检测两人相似度的最优方法是什么？视频统计唯一人物效果不佳求助

Hey there! Let's tackle your two questions with practical, actionable advice tailored to your use case:

For human face similarity (which I assume is what you're targeting, since you're working with people in videos), the best approach is to use specialized face embedding models trained explicitly on face recognition tasks. Here's why and what to use:

• These models convert faces into high-dimensional numerical vectors (embeddings) where vectors of the same person are close to each other, even with variations in lighting, pose, or expression.
• Top choices include:
  • FaceNet: Pioneered the triplet loss approach for face embedding, ideal for general face recognition scenarios.
  • ArcFace: Uses angular margin loss to boost separation between different individuals, one of the most accurate models for face identity matching.
  • InsightFace: A collection of state-of-the-art face models with pre-trained weights ready for immediate use.
• Once you have the embeddings, use cosine similarity (not MSE or Euclidean distance alone) to measure how close two face vectors are—this is far more robust to the real-world variations you'll encounter in videos.

It makes total sense that MSE, SIFT, and Deep Ranking didn't work well—they're not built to handle the unique challenges of face identity in videos. Here's how to fix this:

1. First, optimize your face detection pipeline #

Bad face detection ruins even the best similarity model. If you're not already doing this:

• Use a dedicated face detector like MTCNN or RetinaFace to accurately crop and align faces from video frames. These handle small faces, partial occlusion, and side profiles way better than generic object detectors.
• Filter out low-quality face crops (blurry, too small) before running similarity checks—garbage in = garbage out.

2. Replace Deep Ranking with a face-specific embedding model #

Deep Ranking is designed for general image similarity (like matching cats to cats), not distinguishing between different people. Swap it for one of the face models mentioned above (ArcFace is a great starting point). You'll immediately see better separation between distinct individuals.

3. Tune your similarity threshold #

No model works out of the box with a one-size-fits-all threshold. Test with your video data:

• Take sample frames of the same person in different conditions (lighting, pose) and calculate their embedding similarity.
• Take samples of different people and note their similarity scores.
• Set a threshold that minimizes false matches (different people being labeled the same) and false rejects (same person being labeled different) for your specific video scenario.

4. Leverage video's temporal information #

Unlike static images, videos have continuous frames. Use a tracking + recognition pipeline:

• First, track faces across frames with an algorithm like DeepSORT (which combines appearance features with motion tracking). This links face detections in consecutive frames to the same "track".
• Then, average the embeddings of all faces in a single track to get a more robust representation of that person. This reduces noise from blurry frames or temporary occlusion.
• Finally, count the number of unique tracks (after merging any tracks that belong to the same person via embedding similarity).

5. Fine-tune if your video has unique characteristics #

If your video is low-resolution, has unusual lighting (like night vision), or specific angles, fine-tune a pre-trained face model on a small labeled dataset from your video:

• Crop faces from your video frames and label which belong to the same person.
• Use this data to fine-tune an ArcFace or InsightFace model—this will make the embeddings much more tailored to your specific use case.

Why your old methods fell short: #

• Mean Square Error (MSE): Compares pixel values directly, so it's useless for faces—lighting changes or a slight head turn will make MSE spike even for the same person.
• SIFT: Extracts local image features, but it can't capture the semantic identity of a face. It's great for matching objects with distinct textures, but not for distinguishing between two people with similar features.
• Deep Ranking: As mentioned, it's a general image similarity model, not optimized for face identity. It might match two faces that look visually similar but are different people, or fail to match the same person in different poses.

内容的提问来源于stack exchange，提问作者Omar Medhat