Initial commit

README.md

@@ -1,13 +1,13 @@
 ---
 title: Face Recognition FHE
-emoji: 🧑 📸 🔐
+emoji: 🧑
 colorFrom: red
 colorTo: blue
 sdk: gradio
 sdk_version: 5.22.0
 app_file: app.py
 pinned: false
-short_description: Face Recognition (1:1 matching) Using FHE
+short_description: Face Recognition (1:1 matching) using FHE
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -40,7 +40,7 @@ FRMODELS = ["gaunernst/vit_tiny_patch8_112.arcface_ms1mv3",
 def runBinFile(*args):
     binary_path = args[0]
     if not os.path.isfile(binary_path):
-        return "Error: Compiled binary not found."
+        return "Error: Compiled binary not Match."
     try:
         os.chmod(binary_path, 0o755)
         start = time.time()
@@ -125,7 +125,28 @@ def styled_output(result):
 
 
 with gr.Blocks() as demo:
-    gr.Markdown("# Biometric Recognition (1:1 matching) Using Fully Homomorphic Encryption (FHE)") 
+    gr.HTML(
+        """
+        <h1 align="center">Suraksh.AI</h1>
+        <p align="center">
+            <a href="https://suraksh-ai.vercel.app/"> https://suraksh-ai.vercel.app/</a>
+        </p>       
+        """
+    )
+    gr.Markdown("# Biometric verification (1:1 matching) Using Fully Homomorphic Encryption (FHE)")
+
+    gr.HTML(
+        """
+        <p>This demo shows <strong>Suraksh.AI's</strong> biometric verification solution under <strong>FHE</strong>.</p>
+        <ul>
+        <li><strong>Scenario 1</strong>: Verifying an enrolled subject. For this scenario, the reference and probe should be from the same subject. Expected outcome: <span style='color: green; font-weight: bold;'>✔️ Match</span></li>
+        <li><strong>Scenario 2</strong>: Verifying an enrolled subject with high recognition threshold. For this scenario, the reference and probe should be from the same subject and increase the recognition threshold. Expected outcome: <span style='color: red; font-weight: bold;'>❌ No Match</span></li>
+        <li><strong>Scenario 3</strong>: Verifying a non-enrolled subject. For this scenario, choose a probe not enrolled. Expected outcome: <span style='color: red; font-weight: bold;'>❌ No Match</span></li>
+        <li><strong>Scenario 4</strong>: Verifying a non-enrolled subject with low recognition threshold. For this scenario, choose a probe not enrolled and lower the recognition threshold. Expected outcome: <span style='color: green; font-weight: bold;'>✔️ Match</span></li>
+        </ul>
+        """
+    )
+
     with gr.Row():
         gr.Markdown("## Phase 1: Enrollment")
     with gr.Row():
@@ -157,7 +178,7 @@ with gr.Blocks() as demo:
 
 
     with gr.Row():
-        gr.HTML("<h3>Facial embeddings are <span style='color:red; font-weight:bold'>INVERTIBLE</span> and lead to the <span style='color:red; font-weight:bold'>RECONSTRUCTION</span> of their raw facial images.</h3>")   
+        gr.Markdown("""Facial embeddings are **INVERTIBLE** and lead to the **RECONSTRUCTION** of their raw facial images.""")   
     with gr.Row():
         gr.Markdown("### Example:")   
     with gr.Row():
@@ -169,7 +190,7 @@ with gr.Blocks() as demo:
         Reconstructed_image = gr.Image(label="Reconstructed")        
         btn.click(fn=load_rec_image, outputs=Reconstructed_image)
     with gr.Row():
-        gr.HTML("<h3>Facial embeddings protection is a <span style='color:red; font-weight:bold'>MUST!</span> At Suraksh.AI, we protect facial embeddings using FHE.</h3>")
+        gr.Markdown("""Facial embeddings protection is a must! At **Suraksh.AI**, we protect facial embeddings using FHE.""")