feat: Implement empirical confidence calibration for face matching

This commit introduces a new confidence calibration system that converts DeepFace distance values into actual match probabilities, addressing previous misleading confidence percentages. Key changes include the addition of calibration methods in `FaceProcessor`, updates to the `IdentifyPanel` and `AutoMatchPanel` to utilize calibrated confidence, and new configuration settings in `config.py`. The README has been updated to document these enhancements, ensuring users see more realistic match probabilities throughout the application.

src/photo_tagger.py

@@ -276,7 +276,7 @@ class PhotoTagger:
                 # Identify mode: filter out both database and session identified faces
                 if not is_identified_in_db and not is_identified_in_session:
                     # Calculate confidence percentage
-                    confidence_pct = (1 - face['distance']) * 100
+                    confidence_pct, _ = self.face_processor._get_calibrated_confidence(face['distance'])
                     
                     # Only include matches with reasonable confidence (at least 40%)
                     if confidence_pct >= 40:
@@ -285,7 +285,7 @@ class PhotoTagger:
                 # Auto-match mode: only filter by database state (keep existing behavior)
                 if not is_identified_in_db:
                     # Calculate confidence percentage
-                    confidence_pct = (1 - face['distance']) * 100
+                    confidence_pct, _ = self.face_processor._get_calibrated_confidence(face['distance'])
                     
                     # Only include matches with reasonable confidence (at least 40%)
                     if confidence_pct >= 40: