adding expand percentage argument for detection

deepface/DeepFace.py

@@ -58,6 +58,7 @@ def verify(
     distance_metric: str = "cosine",
     enforce_detection: bool = True,
     align: bool = True,
+    expand_percentage: int = 0,
     normalization: str = "base",
 ) -> Dict[str, Any]:
     """
@@ -83,6 +84,8 @@ def verify(
 
         align (bool): Flag to enable face alignment (default is True).
 
+        expand_percentage (int): expand detected facial area with a percentage (default is 0).
+
         normalization (string): Normalize the input image before feeding it to the model.
             Options: base, raw, Facenet, Facenet2018, VGGFace, VGGFace2, ArcFace (default is base)
 
@@ -119,6 +122,7 @@ def verify(
         distance_metric=distance_metric,
         enforce_detection=enforce_detection,
         align=align,
+        expand_percentage=expand_percentage,
         normalization=normalization,
     )
 
@@ -129,6 +133,7 @@ def analyze(
     enforce_detection: bool = True,
     detector_backend: str = "opencv",
     align: bool = True,
+    expand_percentage: int = 0,
     silent: bool = False,
 ) -> List[Dict[str, Any]]:
     """
@@ -152,6 +157,8 @@ def analyze(
 
         align (boolean): Perform alignment based on the eye positions (default is True).
 
+        expand_percentage (int): expand detected facial area with a percentage (default is 0).
+
         silent (boolean): Suppress or allow some log messages for a quieter analysis process
             (default is False).
 
@@ -209,6 +216,7 @@ def analyze(
         enforce_detection=enforce_detection,
         detector_backend=detector_backend,
         align=align,
+        expand_percentage=expand_percentage,
         silent=silent,
     )
 
@@ -221,6 +229,7 @@ def find(
     enforce_detection: bool = True,
     detector_backend: str = "opencv",
     align: bool = True,
+    expand_percentage: int = 0,
     threshold: Optional[float] = None,
     normalization: str = "base",
     silent: bool = False,
@@ -249,6 +258,8 @@ def find(
 
         align (boolean): Perform alignment based on the eye positions (default is True).
 
+        expand_percentage (int): expand detected facial area with a percentage (default is 0).
+
         threshold (float): Specify a threshold to determine whether a pair represents the same
             person or different individuals. This threshold is used for comparing distances.
             If left unset, default pre-tuned threshold values will be applied based on the specified
@@ -286,6 +297,7 @@ def find(
         enforce_detection=enforce_detection,
         detector_backend=detector_backend,
         align=align,
+        expand_percentage=expand_percentage,
         threshold=threshold,
         normalization=normalization,
         silent=silent,
@@ -298,6 +310,7 @@ def represent(
     enforce_detection: bool = True,
     detector_backend: str = "opencv",
     align: bool = True,
+    expand_percentage: int = 0,
     normalization: str = "base",
 ) -> List[Dict[str, Any]]:
     """
@@ -320,6 +333,8 @@ def represent(
 
         align (boolean): Perform alignment based on the eye positions (default is True).
 
+        expand_percentage (int): expand detected facial area with a percentage (default is 0).
+
         normalization (string): Normalize the input image before feeding it to the model.
             Default is base. Options: base, raw, Facenet, Facenet2018, VGGFace, VGGFace2, ArcFace
             (default is base).
@@ -346,6 +361,7 @@ def represent(
         enforce_detection=enforce_detection,
         detector_backend=detector_backend,
         align=align,
+        expand_percentage=expand_percentage,
         normalization=normalization,
     )
 
@@ -409,6 +425,7 @@ def extract_faces(
     detector_backend: str = "opencv",
     enforce_detection: bool = True,
     align: bool = True,
+    expand_percentage: int = 0,
     grayscale: bool = False,
 ) -> List[Dict[str, Any]]:
     """
@@ -429,6 +446,8 @@ def extract_faces(
 
         align (bool): Flag to enable face alignment (default is True).
 
+        expand_percentage (int): expand detected facial area with a percentage (default is 0).
+
         grayscale (boolean): Flag to convert the image to grayscale before
             processing (default is False).
 
@@ -448,6 +467,7 @@ def extract_faces(
         detector_backend=detector_backend,
         enforce_detection=enforce_detection,
         align=align,
+        expand_percentage=expand_percentage,
         grayscale=grayscale,
         human_readable=True,
     )

deepface/detectors/DetectorWrapper.py

@@ -12,6 +12,9 @@ from deepface.detectors import (
     Yolo,
     YuNet,
 )
+from deepface.commons.logger import Logger
+
+logger = Logger(module="deepface/detectors/DetectorWrapper.py")
 
 
 def build_model(detector_backend: str) -> Any:
@@ -52,19 +55,35 @@ def build_model(detector_backend: str) -> Any:
     return face_detector_obj[detector_backend]
 
 
-def detect_faces(detector_backend: str, img: np.ndarray, align: bool = True) -> List[DetectedFace]:
+def detect_faces(
+    detector_backend: str, img: np.ndarray, align: bool = True, expand_percentage: int = 0
+) -> List[DetectedFace]:
     """
     Detect face(s) from a given image
     Args:
         detector_backend (str): detector name
+
         img (np.ndarray): pre-loaded image
-        alig (bool): enable or disable alignment after detection
+
+        align (bool): enable or disable alignment after detection
+
+        expand_percentage (int): expand detected facial area with a percentage (default is 0).
+
     Returns:
         results (List[DetectedFace]): A list of DetectedFace objects
             where each object contains:
+
         - img (np.ndarray): The detected face as a NumPy array.
+
         - facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
+
         - confidence (float): The confidence score associated with the detected face.
     """
     face_detector: Detector = build_model(detector_backend)
-    return face_detector.detect_faces(img=img, align=align)
+    if expand_percentage < 0:
+        logger.warn(
+            f"Expand percentage cannot be negative but you set it to {expand_percentage}."
+            "Overwritten it to 0."
+        )
+        expand_percentage = 0
+    return face_detector.detect_faces(img=img, align=align, expand_percentage=expand_percentage)

deepface/detectors/Dlib.py

@@ -56,18 +56,27 @@ class DlibClient(Detector):
         detector["sp"] = sp
         return detector
 
-    def detect_faces(self, img: np.ndarray, align: bool = True) -> List[DetectedFace]:
+    def detect_faces(
+        self, img: np.ndarray, align: bool = True, expand_percentage: int = 0
+    ) -> List[DetectedFace]:
         """
         Detect and align face with dlib
+
         Args:
-            face_detector (Any): dlib face detector object
+            img (np.ndarray): pre-loaded image as numpy array
-            img (np.ndarray): pre-loaded image
+
-            align (bool): default is true
+            align (bool): flag to enable or disable alignment after detection (default is True)
+
+            expand_percentage (int): expand detected facial area with a percentage
+
         Returns:
-            results (List[DetectedFace]): A list of DetectedFace objects
+            results (List[Tuple[DetectedFace]): A list of DetectedFace objects
                 where each object contains:
+
             - img (np.ndarray): The detected face as a NumPy array.
+
             - facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
+
             - confidence (float): The confidence score associated with the detected face.
         """
         # this is not a must dependency. do not import it in the global level.
@@ -79,6 +88,12 @@ class DlibClient(Detector):
                 "Please install using 'pip install dlib' "
             ) from e
 
+        if expand_percentage != 0:
+            logger.warn(
+                f"You set expand_percentage argument to {expand_percentage},"
+                "but dlib hog handles detection by itself"
+            )
+
         resp = []
 
         sp = self.model["sp"]

deepface/detectors/FastMtCnn.py

@@ -12,17 +12,27 @@ class FastMtCnnClient(Detector):
     def __init__(self):
         self.model = self.build_model()
 
-    def detect_faces(self, img: np.ndarray, align: bool = True) -> List[DetectedFace]:
+    def detect_faces(
+        self, img: np.ndarray, align: bool = True, expand_percentage: int = 0
+    ) -> List[DetectedFace]:
         """
         Detect and align face with mtcnn
+
         Args:
-            img (np.ndarray): pre-loaded image
+            img (np.ndarray): pre-loaded image as numpy array
-            align (bool): default is true
+
+            align (bool): flag to enable or disable alignment after detection (default is True)
+
+            expand_percentage (int): expand detected facial area with a percentage
+
         Returns:
-            results (List[DetectedFace]): A list of DetectedFace objects
+            results (List[Tuple[DetectedFace]): A list of DetectedFace objects
                 where each object contains:
+
             - img (np.ndarray): The detected face as a NumPy array.
+
             - facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
+
             - confidence (float): The confidence score associated with the detected face.
         """
         resp = []
@@ -37,7 +47,16 @@ class FastMtCnnClient(Detector):
 
             for current_detection in zip(*detections):
                 x, y, w, h = xyxy_to_xywh(current_detection[0])
-                detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
+
+                # expand the facial area to be extracted and stay within img.shape limits
+                x2 = max(0, x - int((w * expand_percentage) / 100))  # expand left
+                y2 = max(0, y - int((h * expand_percentage) / 100))  # expand top
+                w2 = min(img.shape[1], w + int((w * expand_percentage) / 100))  # expand right
+                h2 = min(img.shape[0], h + int((h * expand_percentage) / 100))  # expand bottom
+
+                # detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
+                detected_face = img[int(y2) : int(y2 + h2), int(x2) : int(x2 + w2)]
+
                 img_region = FacialAreaRegion(x=x, y=y, w=w, h=h)
                 confidence = current_detection[1]
 

deepface/detectors/MediaPipe.py

@@ -29,17 +29,27 @@ class MediaPipeClient(Detector):
         face_detection = mp_face_detection.FaceDetection(min_detection_confidence=0.7)
         return face_detection
 
-    def detect_faces(self, img: np.ndarray, align: bool = True) -> List[DetectedFace]:
+    def detect_faces(
+        self, img: np.ndarray, align: bool = True, expand_percentage: int = 0
+    ) -> List[DetectedFace]:
         """
         Detect and align face with mediapipe
+
         Args:
-            img (np.ndarray): pre-loaded image
+            img (np.ndarray): pre-loaded image as numpy array
-            align (bool): default is true
+
+            align (bool): flag to enable or disable alignment after detection (default is True)
+
+            expand_percentage (int): expand detected facial area with a percentage
+
         Returns:
-            results (List[DetectedFace): A list of DetectedFace objects
+            results (List[Tuple[DetectedFace]): A list of DetectedFace objects
                 where each object contains:
+
             - img (np.ndarray): The detected face as a NumPy array.
+
             - facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
+
             - confidence (float): The confidence score associated with the detected face.
         """
         resp = []
@@ -74,7 +84,16 @@ class MediaPipeClient(Detector):
             # left_ear = (int(landmarks[5].x * img_width), int(landmarks[5].y * img_height))
 
             if x > 0 and y > 0:
-                detected_face = img[y : y + h, x : x + w]
+
+                # expand the facial area to be extracted and stay within img.shape limits
+                x2 = max(0, x - int((w * expand_percentage) / 100))  # expand left
+                y2 = max(0, y - int((h * expand_percentage) / 100))  # expand top
+                w2 = min(img.shape[1], w + int((w * expand_percentage) / 100))  # expand right
+                h2 = min(img.shape[0], h + int((h * expand_percentage) / 100))  # expand bottom
+
+                # detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
+                detected_face = img[int(y2) : int(y2 + h2), int(x2) : int(x2 + w2)]
+
                 img_region = FacialAreaRegion(x=x, y=y, w=w, h=h)
 
                 if align:

deepface/detectors/MtCnn.py

@@ -13,17 +13,27 @@ class MtCnnClient(Detector):
     def __init__(self):
         self.model = MTCNN()
 
-    def detect_faces(self, img: np.ndarray, align: bool = True) -> List[DetectedFace]:
+    def detect_faces(
+        self, img: np.ndarray, align: bool = True, expand_percentage: int = 0
+    ) -> List[DetectedFace]:
         """
         Detect and align face with mtcnn
+
         Args:
-            img (np.ndarray): pre-loaded image
+            img (np.ndarray): pre-loaded image as numpy array
-            align (bool): default is true
+
+            align (bool): flag to enable or disable alignment after detection (default is True)
+
+            expand_percentage (int): expand detected facial area with a percentage
+
         Returns:
-            results (List[DetectedFace]): A list of DetectedFace objects
+            results (List[Tuple[DetectedFace]): A list of DetectedFace objects
                 where each object contains:
+
             - img (np.ndarray): The detected face as a NumPy array.
+
             - facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
+
             - confidence (float): The confidence score associated with the detected face.
         """
 
@@ -40,7 +50,16 @@ class MtCnnClient(Detector):
 
             for current_detection in detections:
                 x, y, w, h = current_detection["box"]
-                detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
+
+                # expand the facial area to be extracted and stay within img.shape limits
+                x2 = max(0, x - int((w * expand_percentage) / 100))  # expand left
+                y2 = max(0, y - int((h * expand_percentage) / 100))  # expand top
+                w2 = min(img.shape[1], w + int((w * expand_percentage) / 100))  # expand right
+                h2 = min(img.shape[0], h + int((h * expand_percentage) / 100))  # expand bottom
+
+                # detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
+                detected_face = img[int(y2) : int(y2 + h2), int(x2) : int(x2 + w2)]
+
                 img_region = FacialAreaRegion(x=x, y=y, w=w, h=h)
                 confidence = current_detection["confidence"]
 

deepface/detectors/OpenCv.py

@@ -25,18 +25,27 @@ class OpenCvClient(Detector):
         detector["eye_detector"] = self.__build_cascade("haarcascade_eye")
         return detector
 
-    def detect_faces(self, img: np.ndarray, align: bool = True) -> List[DetectedFace]:
+    def detect_faces(
+        self, img: np.ndarray, align: bool = True, expand_percentage: int = 0
+    ) -> List[DetectedFace]:
         """
         Detect and align face with opencv
+
         Args:
-            face_detector (Any): opencv face detector object
+            img (np.ndarray): pre-loaded image as numpy array
-            img (np.ndarray): pre-loaded image
+
-            align (bool): default is true
+            align (bool): flag to enable or disable alignment after detection (default is True)
+
+            expand_percentage (int): expand detected facial area with a percentage
+
         Returns:
             results (List[Tuple[DetectedFace]): A list of DetectedFace objects
                 where each object contains:
+
             - img (np.ndarray): The detected face as a NumPy array.
+
             - facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
+
             - confidence (float): The confidence score associated with the detected face.
         """
         resp = []
@@ -56,7 +65,15 @@ class OpenCvClient(Detector):
 
         if len(faces) > 0:
             for (x, y, w, h), confidence in zip(faces, scores):
-                detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
+
+                # expand the facial area to be extracted and stay within img.shape limits
+                x2 = max(0, x - int((w * expand_percentage) / 100))  # expand left
+                y2 = max(0, y - int((h * expand_percentage) / 100))  # expand top
+                w2 = min(img.shape[1], w + int((w * expand_percentage) / 100))  # expand right
+                h2 = min(img.shape[0], h + int((h * expand_percentage) / 100))  # expand bottom
+
+                # detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
+                detected_face = img[int(y2) : int(y2 + h2), int(x2) : int(x2 + w2)]
 
                 if align:
                     left_eye, right_eye = self.find_eyes(img=detected_face)

deepface/detectors/RetinaFace.py

@@ -9,17 +9,27 @@ class RetinaFaceClient(Detector):
     def __init__(self):
         self.model = rf.build_model()
 
-    def detect_faces(self, img: np.ndarray, align: bool = True) -> List[DetectedFace]:
+    def detect_faces(
+        self, img: np.ndarray, align: bool = True, expand_percentage: int = 0
+    ) -> List[DetectedFace]:
         """
         Detect and align face with retinaface
+
         Args:
-            img (np.ndarray): pre-loaded image
+            img (np.ndarray): pre-loaded image as numpy array
-            align (bool): default is true
+
+            align (bool): flag to enable or disable alignment after detection (default is True)
+
+            expand_percentage (int): expand detected facial area with a percentage
+
         Returns:
-            results (List[DetectedFace]): A list of DetectedFace object
+            results (List[Tuple[DetectedFace]): A list of DetectedFace objects
                 where each object contains:
+
             - img (np.ndarray): The detected face as a NumPy array.
+
             - facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
+
             - confidence (float): The confidence score associated with the detected face.
         """
         resp = []
@@ -38,10 +48,14 @@ class RetinaFaceClient(Detector):
                 img_region = FacialAreaRegion(x=x, y=y, w=w, h=h)
                 confidence = identity["score"]
 
-                # detected_face = img[int(y):int(y+h), int(x):int(x+w)] #opencv
+                # expand the facial area to be extracted and stay within img.shape limits
-                detected_face = img[
+                x2 = max(0, x - int((w * expand_percentage) / 100))  # expand left
-                    facial_area[1] : facial_area[3], facial_area[0] : facial_area[2]
+                y2 = max(0, y - int((h * expand_percentage) / 100))  # expand top
-                ]
+                w2 = min(img.shape[1], w + int((w * expand_percentage) / 100))  # expand right
+                h2 = min(img.shape[0], h + int((h * expand_percentage) / 100))  # expand bottom
+
+                # detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
+                detected_face = img[int(y2) : int(y2 + h2), int(x2) : int(x2 + w2)]
 
                 if align:
                     landmarks = identity["landmarks"]

deepface/detectors/Ssd.py

@@ -71,17 +71,27 @@ class SsdClient(Detector):
 
         return detector
 
-    def detect_faces(self, img: np.ndarray, align: bool = True) -> List[DetectedFace]:
+    def detect_faces(
+        self, img: np.ndarray, align: bool = True, expand_percentage: int = 0
+    ) -> List[DetectedFace]:
         """
         Detect and align face with ssd
+
         Args:
-            img (np.ndarray): pre-loaded image
+            img (np.ndarray): pre-loaded image as numpy array
-            align (bool): default is true
+
+            align (bool): flag to enable or disable alignment after detection (default is True)
+
+            expand_percentage (int): expand detected facial area with a percentage
+
         Returns:
-            results (List[DetectedFace]): A list of DetectedFace object
+            results (List[Tuple[DetectedFace]): A list of DetectedFace objects
                 where each object contains:
+
             - img (np.ndarray): The detected face as a NumPy array.
+
             - facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
+
             - confidence (float): The confidence score associated with the detected face.
         """
         resp = []
@@ -92,16 +102,14 @@ class SsdClient(Detector):
 
         target_size = (300, 300)
 
-        base_img = img.copy()  # we will restore base_img to img later
-
         original_size = img.shape
 
-        img = cv2.resize(img, target_size)
+        current_img = cv2.resize(img, target_size)
 
         aspect_ratio_x = original_size[1] / target_size[1]
         aspect_ratio_y = original_size[0] / target_size[0]
 
-        imageBlob = cv2.dnn.blobFromImage(image=img)
+        imageBlob = cv2.dnn.blobFromImage(image=current_img)
 
         face_detector = self.model["face_detector"]
         face_detector.setInput(imageBlob)
@@ -126,17 +134,21 @@ class SsdClient(Detector):
                 bottom = instance["bottom"]
                 top = instance["top"]
 
-                detected_face = base_img[
+                x = int(left * aspect_ratio_x)
-                    int(top * aspect_ratio_y) : int(bottom * aspect_ratio_y),
+                y = int(top * aspect_ratio_y)
-                    int(left * aspect_ratio_x) : int(right * aspect_ratio_x),
+                w = int(right * aspect_ratio_x) - int(left * aspect_ratio_x)
-                ]
+                h = int(bottom * aspect_ratio_y) - int(top * aspect_ratio_y)
 
-                face_region = FacialAreaRegion(
+                # expand the facial area to be extracted and stay within img.shape limits
-                    x=int(left * aspect_ratio_x),
+                x2 = max(0, x - int((w * expand_percentage) / 100))  # expand left
-                    y=int(top * aspect_ratio_y),
+                y2 = max(0, y - int((h * expand_percentage) / 100))  # expand top
-                    w=int(right * aspect_ratio_x) - int(left * aspect_ratio_x),
+                w2 = min(img.shape[1], w + int((w * expand_percentage) / 100))  # expand right
-                    h=int(bottom * aspect_ratio_y) - int(top * aspect_ratio_y),
+                h2 = min(img.shape[0], h + int((h * expand_percentage) / 100))  # expand bottom
-                )
+
+                detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
+                detected_face = img[int(y2) : int(y2 + h2), int(x2) : int(x2 + w2)]
+
+                face_region = FacialAreaRegion(x=x, y=y, w=w, h=h)
 
                 confidence = instance["confidence"]
 

deepface/detectors/Yolo.py

@@ -51,18 +51,27 @@ class YoloClient(Detector):
         # Return face_detector
         return YOLO(weight_path)
 
-    def detect_faces(self, img: np.ndarray, align: bool = False) -> List[DetectedFace]:
+    def detect_faces(
+        self, img: np.ndarray, align: bool = False, expand_percentage: int = 0
+    ) -> List[DetectedFace]:
         """
         Detect and align face with yolo
+
         Args:
-            face_detector (Any): yolo face detector object
+            img (np.ndarray): pre-loaded image as numpy array
-            img (np.ndarray): pre-loaded image
+
-            align (bool): default is true
+            align (bool): flag to enable or disable alignment after detection (default is True)
+
+            expand_percentage (int): expand detected facial area with a percentage
+
         Returns:
             results (List[Tuple[DetectedFace]): A list of DetectedFace objects
                 where each object contains:
+
             - img (np.ndarray): The detected face as a NumPy array.
+
             - facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
+
             - confidence (float): The confidence score associated with the detected face.
         """
         resp = []
@@ -78,7 +87,15 @@ class YoloClient(Detector):
 
             x, y, w, h = int(x - w / 2), int(y - h / 2), int(w), int(h)
             region = FacialAreaRegion(x=x, y=y, w=w, h=h)
-            detected_face = img[y : y + h, x : x + w].copy()
+
+            # expand the facial area to be extracted and stay within img.shape limits
+            x2 = max(0, x - int((w * expand_percentage) / 100))  # expand left
+            y2 = max(0, y - int((h * expand_percentage) / 100))  # expand top
+            w2 = min(img.shape[1], w + int((w * expand_percentage) / 100))  # expand right
+            h2 = min(img.shape[0], h + int((h * expand_percentage) / 100))  # expand bottom
+
+            # detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
+            detected_face = img[int(y2) : int(y2 + h2), int(x2) : int(x2 + w2)]
 
             if align:
                 # Tuple of x,y and confidence for left eye

deepface/detectors/YuNet.py

@@ -49,17 +49,27 @@ class YuNetClient(Detector):
             ) from err
         return face_detector
 
-    def detect_faces(self, img: np.ndarray, align: bool = True) -> List[DetectedFace]:
+    def detect_faces(
+        self, img: np.ndarray, align: bool = True, expand_percentage: int = 0
+    ) -> List[DetectedFace]:
         """
         Detect and align face with yunet
+
         Args:
-            img (np.ndarray): pre-loaded image
+            img (np.ndarray): pre-loaded image as numpy array
-            align (bool): default is true
+
+            align (bool): flag to enable or disable alignment after detection (default is True)
+
+            expand_percentage (int): expand detected facial area with a percentage
+
         Returns:
-            results (List[DetectedFace]): A list of DetectedFace objects
+            results (List[Tuple[DetectedFace]): A list of DetectedFace objects
                 where each object contains:
+
             - img (np.ndarray): The detected face as a NumPy array.
+
             - facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
+
             - confidence (float): The confidence score associated with the detected face.
         """
         # FaceDetector.detect_faces does not support score_threshold parameter.
@@ -115,7 +125,16 @@ class YuNetClient(Detector):
                 )
             confidence = face[-1]
             confidence = f"{confidence:.2f}"
-            detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
+
+            # expand the facial area to be extracted and stay within img.shape limits
+            x2 = max(0, x - int((w * expand_percentage) / 100))  # expand left
+            y2 = max(0, y - int((h * expand_percentage) / 100))  # expand top
+            w2 = min(img.shape[1], w + int((w * expand_percentage) / 100))  # expand right
+            h2 = min(img.shape[0], h + int((h * expand_percentage) / 100))  # expand bottom
+
+            # detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
+            detected_face = img[int(y2) : int(y2 + h2), int(x2) : int(x2 + w2)]
+
             img_region = FacialAreaRegion(x=x, y=y, w=w, h=h)
             if align:
                 detected_face = detection.align_face(detected_face, (x_re, y_re), (x_le, y_le))

deepface/models/Detector.py

@@ -8,18 +8,27 @@ import numpy as np
 # pylint: disable=unnecessary-pass, too-few-public-methods
 class Detector(ABC):
     @abstractmethod
-    def detect_faces(self, img: np.ndarray, align: bool = True) -> List["DetectedFace"]:
+    def detect_faces(
+        self, img: np.ndarray, align: bool = True, expand_percentage: int = 0
+    ) -> List["DetectedFace"]:
         """
-        Detect faces from a given image
+        Interface for detect and align face
+
         Args:
-            img (np.ndarray): pre-loaded image as a NumPy array
+            img (np.ndarray): pre-loaded image as numpy array
-            align (bool): enable or disable alignment after face detection
+
+            align (bool): flag to enable or disable alignment after detection (default is True)
+
+            expand_percentage (int): expand detected facial area with a percentage
+
         Returns:
-            results (List[DetectedFace]): A list of DetectedFace object
+            results (List[Tuple[DetectedFace]): A list of DetectedFace objects
                 where each object contains:
-                - face (np.ndarray): The detected face as a NumPy array.
+
-                - face_region (List[float]): The image region represented as
+            - img (np.ndarray): The detected face as a NumPy array.
-                    a list of floats e.g. [x, y, w, h]
+
+            - facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
+
             - confidence (float): The confidence score associated with the detected face.
         """
         pass

deepface/modules/demography.py

@@ -16,6 +16,7 @@ def analyze(
     enforce_detection: bool = True,
     detector_backend: str = "opencv",
     align: bool = True,
+    expand_percentage: int = 0,
     silent: bool = False,
 ) -> List[Dict[str, Any]]:
     """
@@ -40,6 +41,8 @@ def analyze(
 
         align (boolean): Perform alignment based on the eye positions (default is True).
 
+        expand_percentage (int): expand detected facial area with a percentage (default is 0).
+
         silent (boolean): Suppress or allow some log messages for a quieter analysis process
             (default is False).
 
@@ -120,6 +123,7 @@ def analyze(
         grayscale=False,
         enforce_detection=enforce_detection,
         align=align,
+        expand_percentage=expand_percentage,
     )
 
     for img_obj in img_objs:

deepface/modules/detection.py

@@ -31,6 +31,7 @@ def extract_faces(
     detector_backend: str = "opencv",
     enforce_detection: bool = True,
     align: bool = True,
+    expand_percentage: int = 0,
     grayscale: bool = False,
     human_readable=False,
 ) -> List[Dict[str, Any]]:
@@ -52,6 +53,8 @@ def extract_faces(
 
         align (bool): Flag to enable face alignment (default is True).
 
+        expand_percentage (int): expand detected facial area with a percentage
+
         grayscale (boolean): Flag to convert the image to grayscale before
             processing (default is False).
 
@@ -75,7 +78,12 @@ def extract_faces(
     if detector_backend == "skip":
         face_objs = [DetectedFace(img=img, facial_area=base_region, confidence=0)]
     else:
-        face_objs = DetectorWrapper.detect_faces(detector_backend, img, align)
+        face_objs = DetectorWrapper.detect_faces(
+            detector_backend=detector_backend,
+            img=img,
+            align=align,
+            expand_percentage=expand_percentage,
+        )
 
     # in case of no face found
     if len(face_objs) == 0 and enforce_detection is True:

deepface/modules/recognition.py

@@ -26,6 +26,7 @@ def find(
     enforce_detection: bool = True,
     detector_backend: str = "opencv",
     align: bool = True,
+    expand_percentage: int = 0,
     threshold: Optional[float] = None,
     normalization: str = "base",
     silent: bool = False,
@@ -55,6 +56,8 @@ def find(
 
         align (boolean): Perform alignment based on the eye positions.
 
+        expand_percentage (int): expand detected facial area with a percentage (default is 0).
+
         threshold (float): Specify a threshold to determine whether a pair represents the same
             person or different individuals. This threshold is used for comparing distances.
             If left unset, default pre-tuned threshold values will be applied based on the specified
@@ -211,6 +214,7 @@ def find(
         grayscale=False,
         enforce_detection=enforce_detection,
         align=align,
+        expand_percentage=expand_percentage,
     )
 
     resp_obj = []
@@ -309,6 +313,7 @@ def __find_bulk_embeddings(
     detector_backend: str = "opencv",
     enforce_detection: bool = True,
     align: bool = True,
+    expand_percentage: int = 0,
     normalization: str = "base",
     silent: bool = False,
 ):
@@ -317,15 +322,24 @@ def __find_bulk_embeddings(
 
     Args:
         employees (list): list of exact image paths
+
         model_name (str): facial recognition model name
-        target_size (tuple): expected input shape of facial
+
-            recognition model
+        target_size (tuple): expected input shape of facial recognition model
+
         detector_backend (str): face detector model name
+
         enforce_detection (bool): set this to False if you
             want to proceed when you cannot detect any face
+
         align (bool): enable or disable alignment of image
             before feeding to facial recognition model
+
+        expand_percentage (int): expand detected facial area with a
+            percentage (default is 0).
+
         normalization (bool): normalization technique
+
         silent (bool): enable or disable informative logging
     Returns:
         representations (list): pivot list of embeddings with
@@ -344,6 +358,7 @@ def __find_bulk_embeddings(
             grayscale=False,
             enforce_detection=enforce_detection,
             align=align,
+            expand_percentage=expand_percentage,
         )
 
         for img_obj in img_objs:

deepface/modules/representation.py

@@ -16,6 +16,7 @@ def represent(
     enforce_detection: bool = True,
     detector_backend: str = "opencv",
     align: bool = True,
+    expand_percentage: int = 0,
     normalization: str = "base",
 ) -> List[Dict[str, Any]]:
     """
@@ -37,6 +38,8 @@ def represent(
 
         align (boolean): Perform alignment based on the eye positions.
 
+        expand_percentage (int): expand detected facial area with a percentage (default is 0).
+
         normalization (string): Normalize the input image before feeding it to the model.
             Default is base. Options: base, raw, Facenet, Facenet2018, VGGFace, VGGFace2, ArcFace
 
@@ -69,6 +72,7 @@ def represent(
             grayscale=False,
             enforce_detection=enforce_detection,
             align=align,
+            expand_percentage=expand_percentage,
         )
     else:  # skip
         # Try load. If load error, will raise exception internal

deepface/modules/verification.py

@@ -19,6 +19,7 @@ def verify(
     distance_metric: str = "cosine",
     enforce_detection: bool = True,
     align: bool = True,
+    expand_percentage: int = 0,
     normalization: str = "base",
 ) -> Dict[str, Any]:
     """
@@ -49,6 +50,8 @@ def verify(
 
         align (bool): Flag to enable face alignment (default is True).
 
+        expand_percentage (int): expand detected facial area with a percentage (default is 0).
+
         normalization (string): Normalize the input image before feeding it to the model.
             Options: base, raw, Facenet, Facenet2018, VGGFace, VGGFace2, ArcFace (default is base)
 
@@ -91,6 +94,7 @@ def verify(
         grayscale=False,
         enforce_detection=enforce_detection,
         align=align,
+        expand_percentage=expand_percentage,
     )
 
     img2_objs = detection.extract_faces(
@@ -100,6 +104,7 @@ def verify(
         grayscale=False,
         enforce_detection=enforce_detection,
         align=align,
+        expand_percentage=expand_percentage,
     )
     # --------------------------------
     distances = []