From ee63d8b902b34e211e3a50c29c9ecd4b33d2e6e8 Mon Sep 17 00:00:00 2001
From: Sefik Ilkin Serengil <serengil@gmail.com>
Date: Sun, 17 Mar 2024 11:52:55 +0000
Subject: [PATCH 1/2] model building simplified, region calculation sorted

---
 deepface/detectors/FastMtCnn.py | 34 +++++++++++++++------------------
 1 file changed, 15 insertions(+), 19 deletions(-)

diff --git a/deepface/detectors/FastMtCnn.py b/deepface/detectors/FastMtCnn.py
index 88340c0..0b97058 100644
--- a/deepface/detectors/FastMtCnn.py
+++ b/deepface/detectors/FastMtCnn.py
@@ -32,12 +32,10 @@ class FastMtCnnClient(Detector):
             and len(detections) > 0
             and not any(detection is None for detection in detections)  # issue 1043
         ):
-            for current_detection in zip(*detections):
-                x, y, w, h = xyxy_to_xywh(current_detection[0])
-                confidence = current_detection[1]
-
-                left_eye = current_detection[2][0]
-                right_eye = current_detection[2][1]
+            for regions, confidence, eyes in zip(*detections):
+                x, y, w, h = xyxy_to_xywh(regions)
+                left_eye = eyes[0]
+                right_eye = eyes[1]
 
                 left_eye = tuple(int(i) for i in left_eye)
                 right_eye = tuple(int(i) for i in right_eye)
@@ -70,21 +68,19 @@ class FastMtCnnClient(Detector):
                 "Please install using 'pip install facenet-pytorch' "
             ) from e
 
-        face_detector = fast_mtcnn(
-            image_size=160,
-            thresholds=[0.6, 0.7, 0.7],  # MTCNN thresholds
-            post_process=True,
-            device="cpu",
-            select_largest=False,  # return result in descending order
-        )
+        face_detector = fast_mtcnn(device="cpu")
         return face_detector
 
 
-def xyxy_to_xywh(xyxy: Union[list, tuple]) -> list:
+def xyxy_to_xywh(regions: Union[list, tuple]) -> tuple:
     """
-    Convert xyxy format to xywh format.
+    Convert (x1, y1, x2, y2) format to (x, y, w, h) format.
+    Args:
+        regions (list or tuple): facial area coordinates as x, y, x+w, y+h
+    Returns:
+        regions (tuple): facial area coordinates as x, y, w, h
     """
-    x, y = xyxy[0], xyxy[1]
-    w = xyxy[2] - x + 1
-    h = xyxy[3] - y + 1
-    return [x, y, w, h]
+    x, y, x_plus_w, y_plus_h = regions[0], regions[1], regions[2], regions[3]
+    w = x_plus_w - x
+    h = y_plus_h - y
+    return (x, y, w, h)

From 5e3d415105d3ed7e5d66733ffa806060270cdbd0 Mon Sep 17 00:00:00 2001
From: Sefik Ilkin Serengil <serengil@gmail.com>
Date: Sun, 17 Mar 2024 11:53:11 +0000
Subject: [PATCH 2/2] accuracy scores of models updated

---
 README.md | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/README.md b/README.md
index f5e9e06..62b9a9f 100644
--- a/README.md
+++ b/README.md
@@ -140,16 +140,16 @@ FaceNet, VGG-Face, ArcFace and Dlib are [overperforming](https://youtu.be/i_MOwv
 
 | Model | Declared LFW Score |
 | ---   | --- |
-| VGG-Face | 98.78% |
-| Facenet | 99.20% |
-| Facenet512 | 99.65% |
-| OpenFace | 93.80% |
+| VGG-Face | 98.9% |
+| Facenet | 99.2% |
+| Facenet512 | 99.6% |
+| OpenFace | 92.9% |
 | DeepID | - |
-| Dlib | 99.38 % |
-| SFace | 99.60% |
-| ArcFace | 99.41% |
-| GhostFaceNet | 99.76 |
-| *Human-beings* | *97.53%* |
+| Dlib | 97.4 % |
+| SFace | 99.5% |
+| ArcFace | 99.5% |
+| GhostFaceNet | 99.7% |
+| *Human-beings* | *97.5%* |
 
 Conducting experiments with those models within DeepFace may reveal disparities compared to the original studies, owing to the adoption of distinct detection or normalization techniques. Furthermore, some models have been released solely with their backbones, lacking pre-trained weights. Thus, we are utilizing their re-implementations instead of the original pre-trained weights.