Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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📸 Face Recognition Dataset (105 Classes)

A curated and cleaned celebrity face dataset used for training and evaluating:

Face Recognition Model (CNN Embeddings + SVM)
Face Recognition Demo App (Streamlit)

This dataset contains 105 identities and ~18,000 manually organized images, formatted for deep-learning–based face recognition pipelines.

📁 Dataset Structure

The dataset follows a simple folder-based classification format:

face_recognition_dataset/
  ├── person_1/
  ├── person_2/
  ├── ...
  └── person_105/

Each folder contains multiple face images for that identity.
This structure is compatible with most ML frameworks and embedding-based models.

📦 Contents

18k+ images
105 celebrity identities
Cleaned, resized, organized folder structure
Suitable for:
- Embedding extraction (FaceNet, ArcFace, etc.)
- Classification (SVM, kNN, cosine similarity)
- Clustering
- Evaluation & benchmarking

🧠 Model Trained on This Dataset

The official model trained on this dataset is available at:

Model Repository: AI-Solutions-KK/face_recognition
Contains:

svc_model.pkl
classes.npy
centroids.npy
Metadata + reproducible training pipeline

The model achieves ~99% accuracy on this dataset.

🚀 Demo App Using This Dataset

A complete interactive app using this dataset is available at:

App Repository: AI-Solutions-KK/face_recognition_model_demo_app

Features:

Image selection browser
Real-time prediction
Training report
Prediction report
Confusion matrix display

The app automatically downloads this dataset inside the Space using snapshot_download().

🧩 Recommended Usage

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="AI-Solutions-KK/face_recognition_dataset",
    repo_type="dataset",
    local_dir="my_dataset",
    local_dir_use_symlinks=False,
)

After download, the dataset will be available at:

my_dataset/face_recognition_dataset/<class>/<image>.jpg

🔧 Suitable For

Face recognition research
Deep metric learning
Identity classification
Transfer learning experiments
Benchmarking models like:
- FaceNet
- ArcFace
- MobileFaceNet
- InsightFace

👤 Author

Developed and organized by Karan (AI-Solutions-KK)
Please ⭐ the repo if you find it useful.

Downloads last month: 1,316

Total file size:

389 MB