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@ -196,7 +196,7 @@ Age model got ± 4.65 MAE; gender model got 97.44% accuracy, 96.29% precision an
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/), [`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.
<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/detector-portfolio-v4.jpg" width="95%" height="95%"></p>
<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/detector-portfolio-v5.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.