--- a
+++ b/ViTPose/demo/face_img_demo.py
@@ -0,0 +1,140 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os
+import warnings
+from argparse import ArgumentParser
+
+from mmpose.apis import (inference_top_down_pose_model, init_pose_model,
+                         vis_pose_result)
+from mmpose.datasets import DatasetInfo
+
+try:
+    import face_recognition
+    has_face_det = True
+except (ImportError, ModuleNotFoundError):
+    has_face_det = False
+
+
+def process_face_det_results(face_det_results):
+    """Process det results, and return a list of bboxes.
+
+    :param face_det_results: (top, right, bottom and left)
+    :return: a list of detected bounding boxes (x,y,x,y)-format
+    """
+
+    person_results = []
+    for bbox in face_det_results:
+        person = {}
+        # left, top, right, bottom
+        person['bbox'] = [bbox[3], bbox[0], bbox[1], bbox[2]]
+        person_results.append(person)
+
+    return person_results
+
+
+def main():
+    """Visualize the demo images.
+
+    Using mmdet to detect the human.
+    """
+    parser = ArgumentParser()
+    parser.add_argument('pose_config', help='Config file for pose')
+    parser.add_argument('pose_checkpoint', help='Checkpoint file for pose')
+    parser.add_argument('--img-root', type=str, default='', help='Image root')
+    parser.add_argument('--img', type=str, default='', help='Image file')
+    parser.add_argument(
+        '--show',
+        action='store_true',
+        default=False,
+        help='whether to show img')
+    parser.add_argument(
+        '--out-img-root',
+        type=str,
+        default='',
+        help='root of the output img file. '
+        'Default not saving the visualization images.')
+    parser.add_argument(
+        '--device', default='cuda:0', help='Device used for inference')
+    parser.add_argument(
+        '--kpt-thr', type=float, default=0.3, help='Keypoint score threshold')
+    parser.add_argument(
+        '--radius',
+        type=int,
+        default=4,
+        help='Keypoint radius for visualization')
+    parser.add_argument(
+        '--thickness',
+        type=int,
+        default=1,
+        help='Link thickness for visualization')
+
+    assert has_face_det, 'Please install face_recognition to run the demo. ' \
+                         '"pip install face_recognition", For more details, ' \
+                         'see https://github.com/ageitgey/face_recognition'
+
+    args = parser.parse_args()
+
+    assert args.show or (args.out_img_root != '')
+    assert args.img != ''
+
+    # build the pose model from a config file and a checkpoint file
+    pose_model = init_pose_model(
+        args.pose_config, args.pose_checkpoint, device=args.device.lower())
+
+    dataset = pose_model.cfg.data['test']['type']
+    dataset_info = pose_model.cfg.data['test'].get('dataset_info', None)
+    if dataset_info is None:
+        warnings.warn(
+            'Please set `dataset_info` in the config.'
+            'Check https://github.com/open-mmlab/mmpose/pull/663 for details.',
+            DeprecationWarning)
+    else:
+        dataset_info = DatasetInfo(dataset_info)
+
+    image_name = os.path.join(args.img_root, args.img)
+
+    # test a single image, the resulting box is (top, right, bottom and left)
+    image = face_recognition.load_image_file(image_name)
+    face_det_results = face_recognition.face_locations(image)
+
+    # keep the person class bounding boxes.
+    face_results = process_face_det_results(face_det_results)
+
+    # optional
+    return_heatmap = False
+
+    # e.g. use ('backbone', ) to return backbone feature
+    output_layer_names = None
+
+    pose_results, returned_outputs = inference_top_down_pose_model(
+        pose_model,
+        image_name,
+        face_results,
+        bbox_thr=None,
+        format='xyxy',
+        dataset=dataset,
+        dataset_info=dataset_info,
+        return_heatmap=return_heatmap,
+        outputs=output_layer_names)
+
+    if args.out_img_root == '':
+        out_file = None
+    else:
+        os.makedirs(args.out_img_root, exist_ok=True)
+        out_file = os.path.join(args.out_img_root, f'vis_{args.img}')
+
+    # show the results
+    vis_pose_result(
+        pose_model,
+        image_name,
+        pose_results,
+        radius=args.radius,
+        thickness=args.thickness,
+        dataset=dataset,
+        dataset_info=dataset_info,
+        kpt_score_thr=args.kpt_thr,
+        show=args.show,
+        out_file=out_file)
+
+
+if __name__ == '__main__':
+    main()