face_recognition

🧠 Face Recognition Model (CNN Embeddings + SVM)

Domain-specific face recognition model using:

• FaceNet (InceptionResnetV1) to extract 512-D face embeddings
• SVM classifier for identity recognition
• Centroid baseline for cosine-similarity checks / open-set support

Designed to run efficiently on CPU, ideal for lightweight deployment and Streamlit apps.

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📦 Artifacts in This Repository

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🚀 Load Model from Hugging Face

from huggingface_hub import hf_hub_download
import joblib
import numpy as np

REPO_ID = "AI-Solutions-KK/face_recognition"

svc_path = hf_hub_download(REPO_ID, "svc_model.pkl")
centroids_path = hf_hub_download(REPO_ID, "centroids.npy")
classes_path = hf_hub_download(REPO_ID, "classes.npy")

svc_model = joblib.load(svc_path)
centroids = np.load(centroids_path)
class_names = np.load(classes_path, allow_pickle=True)

print("Model loaded successfully. Classes:", len(class_names))

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🔮 Simple Inference Example (Using FaceNet Embeddings)

from huggingface_hub import hf_hub_download
import joblib, numpy as np, cv2, torch
from facenet_pytorch import InceptionResnetV1, MTCNN

REPO_ID = "AI-Solutions-KK/face_recognition"

# Load classifier + metadata
svc_path = hf_hub_download(REPO_ID, "svc_model.pkl")
classes_path = hf_hub_download(REPO_ID, "classes.npy")

obj = joblib.load(svc_path)
svc_model = obj["clf"]
normalizer = obj["norm"]
label_encoder = obj["le"]
class_names = np.load(classes_path, allow_pickle=True)

# Load FaceNet backbone + face detector
device = "cpu"
mtcnn = MTCNN(keep_all=False, device=device)
facenet = InceptionResnetV1(pretrained="vggface2").eval().to(device)

def get_embedding(img_path: str) -> np.ndarray:
    img_bgr = cv2.imread(img_path)
    if img_bgr is None:
        raise ValueError(f"Could not read image: {img_path}")
    img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
    face = mtcnn(img_rgb)
    if face is None:
        raise ValueError("No face detected.")
    if face.dim() == 3:
        face = face.unsqueeze(0)
    with torch.no_grad():
        emb = facenet(face.to(device)).cpu().numpy()
    return emb

def predict_face(img_path: str):
    emb = get_embedding(img_path)
    emb_norm = normalizer.transform(emb)
    probs = svc_model.predict_proba(emb_norm)[0]
    idx = np.argmax(probs)
    label = label_encoder.inverse_transform([idx])[0]
    confidence = float(probs[idx])
    return label, confidence

# -------- RUN ----------
img_path = "test.jpg"
label, prob = predict_face(img_path)
print("Predicted Identity:", label)
print("Confidence Score:", prob)

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⚠️ Important: Domain-Specific / Closed-Set Model

• This SVM is trained on 105 specific identities from the dataset
  AI-Solutions-KK/face_recognition_dataset.
• It will always predict one of these 105 classes, even for unseen people.
• For new datasets / new identities, you must retrain:
  1. Compute new embeddings
  2. Train SVM
  3. Save: svc_model.pkl, classes.npy, centroids.npy

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🔗 Related Repositories & Live Demo

• Dataset Repo
  https://huggingface.co/datasets/AI-Solutions-KK/face_recognition_dataset

• Demo App (Hugging Face)
  https://huggingface.co/spaces/AI-Solutions-KK/face_recognition_model_demo_app

• Stable Public Streamlit App
  https://facerecognition-tq32v5qkt4ltslejzwymw8.streamlit.app/

• Full Training Code & Documentation
  https://github.com/AI-Solutions-KK/face_recognition_cnn_svm

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🧑‍🔧 Train on Your Own Dataset

1. Prepare dataset (root/class_name/image.jpg)
2. Extract embeddings (FaceNet or your own)
3. Train SVM or cosine classifier
4. Save:
   • svc_model.pkl
   • classes.npy
   • centroids.npy

Then plug into your own app or the provided Streamlit demo.

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👤 Author

Karan (AI-Solutions-KK)