Merge pull request #1184 from serengil/feat-task-1204-centerface

Feat task 1204 centerface

README.md

@@ -194,9 +194,9 @@ Age model got ± 4.65 MAE; gender model got 97.44% accuracy, 96.29% precision an
 
 **Face Detectors** - [`Demo`](https://youtu.be/GZ2p2hj2H5k)
 
-Face detection and alignment are important early stages of a modern face recognition pipeline. Experiments show that just alignment increases the face recognition accuracy almost 1%. [`OpenCV`](https://sefiks.com/2020/02/23/face-alignment-for-face-recognition-in-python-within-opencv/), [`SSD`](https://sefiks.com/2020/08/25/deep-face-detection-with-opencv-in-python/), [`Dlib`](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/),  [`MTCNN`](https://sefiks.com/2020/09/09/deep-face-detection-with-mtcnn-in-python/), [`Faster MTCNN`](https://github.com/timesler/facenet-pytorch), [`RetinaFace`](https://sefiks.com/2021/04/27/deep-face-detection-with-retinaface-in-python/), [`MediaPipe`](https://sefiks.com/2022/01/14/deep-face-detection-with-mediapipe/), [`YOLOv8 Face`](https://github.com/derronqi/yolov8-face) and [`YuNet`](https://github.com/ShiqiYu/libfacedetection) detectors are wrapped in deepface.
+Face detection and alignment are important early stages of a modern face recognition pipeline. Experiments show that just alignment increases the face recognition accuracy almost 1%. [`OpenCV`](https://sefiks.com/2020/02/23/face-alignment-for-face-recognition-in-python-within-opencv/), [`Ssd`](https://sefiks.com/2020/08/25/deep-face-detection-with-opencv-in-python/), [`Dlib`](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/),  [`MtCnn`](https://sefiks.com/2020/09/09/deep-face-detection-with-mtcnn-in-python/), `Faster MTCNN`, [`RetinaFace`](https://sefiks.com/2021/04/27/deep-face-detection-with-retinaface-in-python/), [`MediaPipe`](https://sefiks.com/2022/01/14/deep-face-detection-with-mediapipe/), `Yolo`, `YuNet` and `CenterFace` detectors are wrapped in deepface.
 
-<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/detector-portfolio-v5.jpg" width="95%" height="95%"></p>
+<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/detector-portfolio-v6.jpg" width="95%" height="95%"></p>
 
 All deepface functions accept an optional detector backend input argument. You can switch among those detectors with this argument. OpenCV is the default detector.
 
@@ -206,11 +206,12 @@ backends = [
   'ssd', 
   'dlib', 
   'mtcnn', 
+  'fastmtcnn',
   'retinaface', 
   'mediapipe',
   'yolov8',
   'yunet',
-  'fastmtcnn',
+  'centerface',
 ]
 
 #face verification
@@ -317,9 +318,7 @@ You can also run these commands if you are running deepface with docker. Please
 
 ## FAQ and Troubleshooting
 
-If you believe you have identified a bug or encountered a limitation in DeepFace that is not covered in the [existing issues](https://github.com/serengil/deepface/issues) or [closed issues](https://github.com/serengil/deepface/issues?q=is%3Aissue+is%3Aclosed), kindly open a new issue. Ensure that your submission includes clear and detailed reproduction steps, such as your Python version, your DeepFace version (provided by `DeepFace.__version__`), versions of dependent packages (provided by pip freeze), specifics of any exception messages, details about how you are calling DeepFace, and the input image(s) you are using.
+If you believe you have identified a bug or encountered a limitation in DeepFace that is not covered in the [existing issues](https://github.com/serengil/deepface/issues) or [closed issues](https://github.com/serengil/deepface/issues?q=is%3Aissue+is%3Aclosed), kindly open a new issue. Ensure that your submission includes clear and detailed reproduction steps, such as your Python version, your DeepFace version, versions of dependent packages (provided by pip freeze), specifics of any exception messages, details about how you are calling DeepFace, and the input image(s) you are using.
-
-Additionally, it is possible to encounter issues due to recently released dependencies, primarily Python itself or TensorFlow. It is recommended to synchronize your dependencies with the versions [specified in my environment](https://github.com/serengil/deepface/blob/master/requirements_local) and [same python version](https://github.com/serengil/deepface/blob/master/Dockerfile#L2) not to have potential compatibility issues.
 
 ## Contribution
 
@@ -351,7 +350,7 @@ If you use deepface in your research for facial recogntion purposes, please cite
   pages        = {23-27},
   year         = {2020},
   doi          = {10.1109/ASYU50717.2020.9259802},
-  url          = {https://doi.org/10.1109/ASYU50717.2020.9259802},
+  url          = {https://ieeexplore.ieee.org/document/9259802},
   organization = {IEEE}
 }
 ```
@@ -366,7 +365,7 @@ If you use deepface in your research for facial attribute analysis purposes such
   pages        = {1-4},
   year         = {2021},
   doi          = {10.1109/ICEET53442.2021.9659697},
-  url          = {https://doi.org/10.1109/ICEET53442.2021.9659697},
+  url          = {https://ieeexplore.ieee.org/document/9659697},
   organization = {IEEE}
 }
 ```
@@ -377,6 +376,10 @@ Also, if you use deepface in your GitHub projects, please add `deepface` in the
 
 DeepFace is licensed under the MIT License - see [`LICENSE`](https://github.com/serengil/deepface/blob/master/LICENSE) for more details.
 
-DeepFace wraps some external face recognition models: [VGG-Face](http://www.robots.ox.ac.uk/~vgg/software/vgg_face/), [Facenet](https://github.com/davidsandberg/facenet/blob/master/LICENSE.md), [OpenFace](https://github.com/iwantooxxoox/Keras-OpenFace/blob/master/LICENSE), [DeepFace](https://github.com/swghosh/DeepFace), [DeepID](https://github.com/Ruoyiran/DeepID/blob/master/LICENSE.md), [ArcFace](https://github.com/leondgarse/Keras_insightface/blob/master/LICENSE), [Dlib](https://github.com/davisking/dlib/blob/master/dlib/LICENSE.txt), [SFace](https://github.com/opencv/opencv_zoo/blob/master/models/face_recognition_sface/LICENSE) and [GhostFaceNet](https://github.com/HamadYA/GhostFaceNets/blob/main/LICENSE). Besides, age, gender and race / ethnicity models were trained on the backbone of VGG-Face with transfer learning. Licence types will be inherited if you are going to use those models. Please check the license types of those models for production purposes.
+DeepFace wraps some external face recognition models: [VGG-Face](http://www.robots.ox.ac.uk/~vgg/software/vgg_face/), [Facenet](https://github.com/davidsandberg/facenet/blob/master/LICENSE.md), [OpenFace](https://github.com/iwantooxxoox/Keras-OpenFace/blob/master/LICENSE), [DeepFace](https://github.com/swghosh/DeepFace), [DeepID](https://github.com/Ruoyiran/DeepID/blob/master/LICENSE.md), [ArcFace](https://github.com/leondgarse/Keras_insightface/blob/master/LICENSE), [Dlib](https://github.com/davisking/dlib/blob/master/dlib/LICENSE.txt), [SFace](https://github.com/opencv/opencv_zoo/blob/master/models/face_recognition_sface/LICENSE) and [GhostFaceNet](https://github.com/HamadYA/GhostFaceNets/blob/main/LICENSE). Besides, age, gender and race / ethnicity models were trained on the backbone of VGG-Face with transfer learning. 
+
+Similarly, DeepFace wraps many face detectors: [OpenCv](https://github.com/opencv/opencv/blob/4.x/LICENSE), [Ssd](https://github.com/opencv/opencv/blob/master/LICENSE), [Dlib](https://github.com/davisking/dlib/blob/master/LICENSE.txt), [MtCnn](https://github.com/ipazc/mtcnn/blob/master/LICENSE), [Fast MtCnn](https://github.com/timesler/facenet-pytorch/blob/master/LICENSE.md), [RetinaFace](https://github.com/serengil/retinaface/blob/master/LICENSE), [MediaPipe](https://github.com/google/mediapipe/blob/master/LICENSE), [YuNet](https://github.com/ShiqiYu/libfacedetection/blob/master/LICENSE), [Yolo](https://github.com/derronqi/yolov8-face/blob/main/LICENSE) and [CenterFace](https://github.com/Star-Clouds/CenterFace/blob/master/LICENSE).
+
+Licence types will be inherited if you are going to use those models. Please check the license types of those models for production purposes.
 
 DeepFace [logo](https://thenounproject.com/term/face-recognition/2965879/) is created by [Adrien Coquet](https://thenounproject.com/coquet_adrien/) and it is licensed under [Creative Commons: By Attribution 3.0 License](https://creativecommons.org/licenses/by/3.0/).

deepface/DeepFace.py

@@ -88,7 +88,8 @@ def verify(
             OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
 
         detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
-            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' or 'skip' (default is opencv).
+            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
+            (default is opencv).
 
         distance_metric (string): Metric for measuring similarity. Options: 'cosine',
             'euclidean', 'euclidean_l2' (default is cosine).
@@ -168,7 +169,8 @@ def analyze(
             Set to False to avoid the exception for low-resolution images (default is True).
 
         detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
-            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' or 'skip' (default is opencv).
+            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
+            (default is opencv).
 
         distance_metric (string): Metric for measuring similarity. Options: 'cosine',
             'euclidean', 'euclidean_l2' (default is cosine).
@@ -272,7 +274,8 @@ def find(
             Set to False to avoid the exception for low-resolution images (default is True).
 
         detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
-            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' or 'skip' (default is opencv).
+            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
+            (default is opencv).
 
         align (boolean): Perform alignment based on the eye positions (default is True).
 
@@ -348,7 +351,8 @@ def represent(
             (default is True).
 
         detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
-            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' or 'skip' (default is opencv).
+            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
+            (default is opencv).
 
         align (boolean): Perform alignment based on the eye positions (default is True).
 
@@ -406,7 +410,8 @@ def stream(
             OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
 
         detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
-            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' or 'skip' (default is opencv).
+            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
+            (default is opencv).
 
         distance_metric (string): Metric for measuring similarity. Options: 'cosine',
             'euclidean', 'euclidean_l2' (default is cosine).
@@ -454,7 +459,8 @@ def extract_faces(
             as a string, numpy array (BGR), or base64 encoded images.
 
         detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
-            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' or 'skip' (default is opencv).
+            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
+            (default is opencv).
 
         enforce_detection (boolean): If no face is detected in an image, raise an exception.
             Set to False to avoid the exception for low-resolution images (default is True).
@@ -520,7 +526,8 @@ def detectFace(
             added to resize the image (default is (224, 224)).
 
         detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
-            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' or 'skip' (default is opencv).
+            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
+            (default is opencv).
 
         enforce_detection (boolean): If no face is detected in an image, raise an exception.
             Set to False to avoid the exception for low-resolution images (default is True).

deepface/detectors/CenterFace.py

@@ -0,0 +1,217 @@
+# built-in dependencies
+import os
+from typing import List
+
+# 3rd party dependencies
+import numpy as np
+import cv2
+import gdown
+
+# project dependencies
+from deepface.commons import folder_utils
+from deepface.models.Detector import Detector, FacialAreaRegion
+from deepface.commons import logger as log
+
+logger = log.get_singletonish_logger()
+
+# pylint: disable=c-extension-no-member
+
+WEIGHTS_URL = "https://github.com/Star-Clouds/CenterFace/raw/master/models/onnx/centerface.onnx"
+
+
+class CenterFaceClient(Detector):
+    def __init__(self):
+        # BUG: model must be flushed for each call
+        # self.model = self.build_model()
+        pass
+
+    def build_model(self):
+        """
+        Download pre-trained weights of CenterFace model if necessary and load built model
+        """
+        weights_path = f"{folder_utils.get_deepface_home()}/.deepface/weights/centerface.onnx"
+        if not os.path.isfile(weights_path):
+            logger.info(f"Downloading CenterFace weights from {WEIGHTS_URL} to {weights_path}...")
+            try:
+                gdown.download(WEIGHTS_URL, weights_path, quiet=False)
+            except Exception as err:
+                raise ValueError(
+                    f"Exception while downloading CenterFace weights from {WEIGHTS_URL}."
+                    f"You may consider to download it to {weights_path} manually."
+                ) from err
+            logger.info(f"CenterFace model is just downloaded to {os.path.basename(weights_path)}")
+
+        return CenterFace(weight_path=weights_path)
+
+    def detect_faces(self, img: np.ndarray) -> List["FacialAreaRegion"]:
+        """
+        Detect and align face with CenterFace
+
+        Args:
+            img (np.ndarray): pre-loaded image as numpy array
+
+        Returns:
+            results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
+        """
+        resp = []
+
+        threshold = float(os.getenv("CENTERFACE_THRESHOLD", "0.80"))
+
+        # BUG: model causes problematic results from 2nd call if it is not flushed
+        # detections, landmarks = self.model.forward(
+        #     img, img.shape[0], img.shape[1], threshold=threshold
+        # )
+        detections, landmarks = self.build_model().forward(
+            img, img.shape[0], img.shape[1], threshold=threshold
+        )
+
+        for i, detection in enumerate(detections):
+            boxes, confidence = detection[:4], detection[4]
+
+            x = boxes[0]
+            y = boxes[1]
+            w = boxes[2] - x
+            h = boxes[3] - y
+
+            landmark = landmarks[i]
+
+            right_eye = (int(landmark[0]), int(landmark[1]))
+            left_eye = (int(landmark[2]), int(landmark[3]))
+            # nose = (int(landmark[4]), int(landmark [5]))
+            # mouth_right = (int(landmark[6]), int(landmark [7]))
+            # mouth_left = (int(landmark[8]), int(landmark [9]))
+
+            facial_area = FacialAreaRegion(
+                x=x,
+                y=y,
+                w=w,
+                h=h,
+                left_eye=left_eye,
+                right_eye=right_eye,
+                confidence=min(max(0, float(confidence)), 1.0),
+            )
+            resp.append(facial_area)
+
+        return resp
+
+
+class CenterFace:
+    """
+    This class is heavily inspired from
+        github.com/Star-Clouds/CenterFace/blob/master/prj-python/centerface.py
+    """
+
+    def __init__(self, weight_path: str):
+        self.net = cv2.dnn.readNetFromONNX(weight_path)
+        self.img_h_new, self.img_w_new, self.scale_h, self.scale_w = 0, 0, 0, 0
+
+    def forward(self, img, height, width, threshold=0.5):
+        self.img_h_new, self.img_w_new, self.scale_h, self.scale_w = self.transform(height, width)
+        return self.inference_opencv(img, threshold)
+
+    def inference_opencv(self, img, threshold):
+        blob = cv2.dnn.blobFromImage(
+            img,
+            scalefactor=1.0,
+            size=(self.img_w_new, self.img_h_new),
+            mean=(0, 0, 0),
+            swapRB=True,
+            crop=False,
+        )
+        self.net.setInput(blob)
+        heatmap, scale, offset, lms = self.net.forward(["537", "538", "539", "540"])
+        return self.postprocess(heatmap, lms, offset, scale, threshold)
+
+    def transform(self, h, w):
+        img_h_new, img_w_new = int(np.ceil(h / 32) * 32), int(np.ceil(w / 32) * 32)
+        scale_h, scale_w = img_h_new / h, img_w_new / w
+        return img_h_new, img_w_new, scale_h, scale_w
+
+    def postprocess(self, heatmap, lms, offset, scale, threshold):
+        dets, lms = self.decode(
+            heatmap, scale, offset, lms, (self.img_h_new, self.img_w_new), threshold=threshold
+        )
+        if len(dets) > 0:
+            dets[:, 0:4:2], dets[:, 1:4:2] = (
+                dets[:, 0:4:2] / self.scale_w,
+                dets[:, 1:4:2] / self.scale_h,
+            )
+            lms[:, 0:10:2], lms[:, 1:10:2] = (
+                lms[:, 0:10:2] / self.scale_w,
+                lms[:, 1:10:2] / self.scale_h,
+            )
+        else:
+            dets = np.empty(shape=[0, 5], dtype=np.float32)
+            lms = np.empty(shape=[0, 10], dtype=np.float32)
+        return dets, lms
+
+    def decode(self, heatmap, scale, offset, landmark, size, threshold=0.1):
+        heatmap = np.squeeze(heatmap)
+        scale0, scale1 = scale[0, 0, :, :], scale[0, 1, :, :]
+        offset0, offset1 = offset[0, 0, :, :], offset[0, 1, :, :]
+        c0, c1 = np.where(heatmap > threshold)
+        boxes, lms = [], []
+        if len(c0) > 0:
+            # pylint:disable=consider-using-enumerate
+            for i in range(len(c0)):
+                s0, s1 = np.exp(scale0[c0[i], c1[i]]) * 4, np.exp(scale1[c0[i], c1[i]]) * 4
+                o0, o1 = offset0[c0[i], c1[i]], offset1[c0[i], c1[i]]
+                s = heatmap[c0[i], c1[i]]
+                x1, y1 = max(0, (c1[i] + o1 + 0.5) * 4 - s1 / 2), max(
+                    0, (c0[i] + o0 + 0.5) * 4 - s0 / 2
+                )
+                x1, y1 = min(x1, size[1]), min(y1, size[0])
+                boxes.append([x1, y1, min(x1 + s1, size[1]), min(y1 + s0, size[0]), s])
+                lm = []
+                for j in range(5):
+                    lm.append(landmark[0, j * 2 + 1, c0[i], c1[i]] * s1 + x1)
+                    lm.append(landmark[0, j * 2, c0[i], c1[i]] * s0 + y1)
+                lms.append(lm)
+            boxes = np.asarray(boxes, dtype=np.float32)
+            keep = self.nms(boxes[:, :4], boxes[:, 4], 0.3)
+            boxes = boxes[keep, :]
+            lms = np.asarray(lms, dtype=np.float32)
+            lms = lms[keep, :]
+        return boxes, lms
+
+    def nms(self, boxes, scores, nms_thresh):
+        x1 = boxes[:, 0]
+        y1 = boxes[:, 1]
+        x2 = boxes[:, 2]
+        y2 = boxes[:, 3]
+        areas = (x2 - x1 + 1) * (y2 - y1 + 1)
+        order = np.argsort(scores)[::-1]
+        num_detections = boxes.shape[0]
+        suppressed = np.zeros((num_detections,), dtype=bool)
+
+        keep = []
+        for _i in range(num_detections):
+            i = order[_i]
+            if suppressed[i]:
+                continue
+            keep.append(i)
+
+            ix1 = x1[i]
+            iy1 = y1[i]
+            ix2 = x2[i]
+            iy2 = y2[i]
+            iarea = areas[i]
+
+            for _j in range(_i + 1, num_detections):
+                j = order[_j]
+                if suppressed[j]:
+                    continue
+
+                xx1 = max(ix1, x1[j])
+                yy1 = max(iy1, y1[j])
+                xx2 = min(ix2, x2[j])
+                yy2 = min(iy2, y2[j])
+                w = max(0, xx2 - xx1 + 1)
+                h = max(0, yy2 - yy1 + 1)
+
+                inter = w * h
+                ovr = inter / (iarea + areas[j] - inter)
+                if ovr >= nms_thresh:
+                    suppressed[j] = True
+
+        return keep

deepface/detectors/DetectorWrapper.py

@@ -12,6 +12,7 @@ from deepface.detectors import (
     Ssd,
     Yolo,
     YuNet,
+    CenterFace,
 )
 from deepface.commons import logger as log
 
@@ -38,6 +39,7 @@ def build_model(detector_backend: str) -> Any:
         "yolov8": Yolo.YoloClient,
         "yunet": YuNet.YuNetClient,
         "fastmtcnn": FastMtCnn.FastMtCnnClient,
+        "centerface": CenterFace.CenterFaceClient,
     }
 
     if not "face_detector_obj" in globals():
@@ -93,7 +95,7 @@ def detect_faces(
         expand_percentage = 0
 
     # find facial areas of given image
-    facial_areas = face_detector.detect_faces(img=img)
+    facial_areas = face_detector.detect_faces(img)
 
     results = []
     for facial_area in facial_areas:

deepface/modules/demography.py

@@ -34,7 +34,8 @@ def analyze(
             Set to False to avoid the exception for low-resolution images (default is True).
 
         detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
-            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' or 'skip' (default is opencv).
+            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
+            (default is opencv).
 
         distance_metric (string): Metric for measuring similarity. Options: 'cosine',
             'euclidean', 'euclidean_l2' (default is cosine).

deepface/modules/detection.py

@@ -33,7 +33,8 @@ def extract_faces(
             as a string, numpy array (BGR), or base64 encoded images.
 
         detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
-            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' or 'skip' (default is opencv)
+            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
+            (default is opencv)
 
         enforce_detection (boolean): If no face is detected in an image, raise an exception.
             Default is True. Set to False to avoid the exception for low-resolution images.

deepface/modules/recognition.py

@@ -52,7 +52,7 @@ def find(
             Default is True. Set to False to avoid the exception for low-resolution images.
 
         detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
-            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' or 'skip'.
+            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'.
 
         align (boolean): Perform alignment based on the eye positions.
 

deepface/modules/representation.py

@@ -33,7 +33,7 @@ def represent(
             Default is True. Set to False to avoid the exception for low-resolution images.
 
         detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
-            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' or 'skip'.
+            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'.
 
         align (boolean): Perform alignment based on the eye positions.
 

deepface/modules/streaming.py

@@ -43,7 +43,8 @@ def analysis(
             OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
 
         detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
-            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' or 'skip' (default is opencv).
+            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
+            (default is opencv).
 
         distance_metric (string): Metric for measuring similarity. Options: 'cosine',
             'euclidean', 'euclidean_l2' (default is cosine).
@@ -182,7 +183,8 @@ def search_identity(
         model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
             OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
         detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
-            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' or 'skip' (default is opencv).
+            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
+            (default is opencv).
         distance_metric (string): Metric for measuring similarity. Options: 'cosine',
             'euclidean', 'euclidean_l2' (default is cosine).
     Returns:
@@ -349,7 +351,8 @@ def grab_facial_areas(
     Args:
         img (np.ndarray): image itself
         detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
-            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' or 'skip' (default is opencv).
+            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
+            (default is opencv).
         threshold (int): threshold for facial area, discard smaller ones
     Returns
         result (list): list of tuple with x, y, w and h coordinates
@@ -414,7 +417,8 @@ def perform_facial_recognition(
         db_path (string): Path to the folder containing image files. All detected faces
             in the database will be considered in the decision-making process.
         detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
-            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' or 'skip' (default is opencv).
+            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
+            (default is opencv).
         distance_metric (string): Metric for measuring similarity. Options: 'cosine',
             'euclidean', 'euclidean_l2' (default is cosine).
         model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,

deepface/modules/verification.py

@@ -45,7 +45,8 @@ def verify(
             OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
 
         detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
-            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' or 'skip' (default is opencv)
+            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
+            (default is opencv)
 
         distance_metric (string): Metric for measuring similarity. Options: 'cosine',
             'euclidean', 'euclidean_l2' (default is cosine).

tests/visual-test.py

@@ -34,9 +34,9 @@ detector_backends = [
     "retinaface",
     "yunet",
     "yolov8",
+    "centerface",
 ]
 
-
 # verification
 for model_name in model_names:
     obj = DeepFace.verify(
@@ -60,7 +60,6 @@ dfs = DeepFace.find(
 for df in dfs:
     logger.info(df)
 
-
 expand_areas = [0]
 img_paths = ["dataset/img11.jpg", "dataset/img11_reflection.jpg"]
 for expand_area in expand_areas:
@@ -75,7 +74,7 @@ for expand_area in expand_areas:
             )
             for face_obj in face_objs:
                 face = face_obj["face"]
-                logger.info(detector_backend)
+                logger.info(f"testing {img_path} with {detector_backend}")
                 logger.info(face_obj["facial_area"])
                 logger.info(face_obj["confidence"])
 
@@ -99,7 +98,10 @@ for expand_area in expand_areas:
                     le_x = face_obj["facial_area"]["left_eye"][0]
                     assert re_x < le_x, "right eye must be the right eye of the person"
 
-                assert isinstance(face_obj["confidence"], float)
+                type_conf = type(face_obj["confidence"])
+                assert isinstance(
+                    face_obj["confidence"], float
+                ), f"confidence type must be float but it is {type_conf}"
                 assert face_obj["confidence"] <= 1
 
                 plt.imshow(face)