diff --git a/common/helpers/numpy2message/doc/usage.md b/common/helpers/numpy2message/doc/usage.md
new file mode 100644
index 000000000..3f19ff99e
--- /dev/null
+++ b/common/helpers/numpy2message/doc/usage.md
@@ -0,0 +1 @@
+To send numpy arrays with messages.
\ No newline at end of file
diff --git a/common/helpers/numpy2message/src/__init__.py b/common/helpers/numpy2message/src/__init__.py
new file mode 100644
index 000000000..3ddad23fe
--- /dev/null
+++ b/common/helpers/numpy2message/src/__init__.py
@@ -0,0 +1,19 @@
+import numpy as np
+
+
+def numpy2message(np_array: np.ndarray) -> list[bytes, list[int], str]:
+    data = np_array.tobytes()
+    shape = list(np_array.shape)
+    dtype = str(np_array.dtype)
+    return data, shape, dtype
+
+
+def message2numpy(data:bytes, shape:list[int], dtype:str) -> np.ndarray:
+    array_shape = tuple(shape)
+    array_dtype = np.dtype(dtype)
+
+    deserialized_array = np.frombuffer(data, dtype=array_dtype)
+    deserialized_array = deserialized_array.reshape(array_shape)
+
+    return deserialized_array
+
diff --git a/common/vision/lasr_vision_msgs/srv/TorchFaceFeatureDetection.srv b/common/vision/lasr_vision_msgs/srv/TorchFaceFeatureDetection.srv
index f71b95aec..e958bcd57 100644
--- a/common/vision/lasr_vision_msgs/srv/TorchFaceFeatureDetection.srv
+++ b/common/vision/lasr_vision_msgs/srv/TorchFaceFeatureDetection.srv
@@ -1,6 +1,13 @@
 # Image to run inference on
 sensor_msgs/Image image_raw
 
+uint8[] head_mask_data  # For serialized array data
+uint32[] head_mask_shape  # To store the shape of the array
+string head_mask_dtype  # Data type of the array elements
+
+uint8[] torso_mask_data
+uint32[] torso_mask_shape
+string torso_mask_dtype
 ---
 
 # Detection result
diff --git a/common/vision/lasr_vision_torch/nodes/service b/common/vision/lasr_vision_torch/nodes/service
index ab8705380..e09829dae 100644
--- a/common/vision/lasr_vision_torch/nodes/service
+++ b/common/vision/lasr_vision_torch/nodes/service
@@ -3,12 +3,14 @@ from lasr_vision_msgs.msg import FeatureWithColour, ColourPrediction
 from colour_estimation import closest_colours, RGB_COLOURS, RGB_HAIR_COLOURS
 from cv2_img import msg_to_cv2_img
 from torch_module.helpers import binary_erosion_dilation, median_color_float
+from numpy2message import message2numpy
 
 import numpy as np
 import torch
 import rospy
 import lasr_vision_torch
 
+
 model = lasr_vision_torch.load_face_classifier_model()
 
 
@@ -16,6 +18,12 @@ def detect(request: TorchFaceFeatureDetectionRequest) -> TorchFaceFeatureDetecti
     # decode the image
     rospy.loginfo('Decoding')
     frame = msg_to_cv2_img(request.image_raw)
+    torso_mask_data, torso_mask_shape, torso_mask_dtype = request.torso_mask_data, request.torso_mask_shape, request.torso_mask_dtype
+    head_mask_data, head_mask_shape, head_mask_dtype = request.head_mask_data, request.head_mask_shape, request.head_mask_dtype 
+    torsal_mask = message2numpy(torso_mask_data, torso_mask_shape, torso_mask_dtype)
+    head_mask = message2numpy(head_mask_data, head_mask_shape, head_mask_dtype)
+    print(torso_mask_shape)
+    print(head_mask_shape)
 
     # 'hair', 'hat', 'glasses', 'face'
     input_image = torch.from_numpy(frame).permute(2, 0, 1).unsqueeze(0).float()
@@ -62,7 +70,59 @@ def detect(request: TorchFaceFeatureDetectionRequest) -> TorchFaceFeatureDetecti
     ]
 
     return response
-#test test
+
+
+# def detect(request: TorchFaceFeatureDetectionRequest) -> TorchFaceFeatureDetectionResponse:
+#     # decode the image
+#     rospy.loginfo('Decoding')
+#     frame = msg_to_cv2_img(request.image_raw)
+
+#     # 'hair', 'hat', 'glasses', 'face'
+#     input_image = torch.from_numpy(frame).permute(2, 0, 1).unsqueeze(0).float()
+#     input_image /= 255.0
+#     masks_batch_pred, pred_classes = model(input_image)
+
+#     thresholds_mask = [
+#         0.5, 0.75, 0.25, 0.5,  # 0.5, 0.5, 0.5, 0.5,
+#     ]
+#     thresholds_pred = [
+#         0.6, 0.8, 0.1, 0.5,
+#     ]
+#     erosion_iterations = 1
+#     dilation_iterations = 1
+#     categories = ['hair', 'hat', 'glasses', 'face',]
+
+#     masks_batch_pred = binary_erosion_dilation(
+#         masks_batch_pred, thresholds=thresholds_mask,
+#         erosion_iterations=erosion_iterations, dilation_iterations=dilation_iterations
+#     )
+
+#     median_colours = (median_color_float(
+#         input_image, masks_batch_pred).detach().squeeze(0)*255).numpy().astype(np.uint8)
+
+#     # discarded: masks = masks_batch_pred.detach().squeeze(0).numpy().astype(np.uint8)
+#     # discarded: mask_list = [masks[i,:,:] for i in range(masks.shape[0])]
+
+#     pred_classes = pred_classes.detach().squeeze(0).numpy()
+#     # discarded: class_list = [categories[i] for i in range(
+#     #     pred_classes.shape[0]) if pred_classes[i].item() > thresholds_pred[i]]
+#     colour_list = [median_colours[i, :]
+#                    for i in range(median_colours.shape[0])]
+
+#     response = TorchFaceFeatureDetectionResponse()
+#     response.detected_features = [
+#         FeatureWithColour(categories[i], [
+#             ColourPrediction(colour, distance)
+#             for colour, distance
+#             in closest_colours(colour_list[i], RGB_HAIR_COLOURS if categories[i] == 'hair' else RGB_COLOURS)
+#         ])
+#         for i
+#         in range(pred_classes.shape[0])
+#         if pred_classes[i].item() > thresholds_pred[i]
+#     ]
+
+#     return response
+# test test
 
 rospy.init_node('torch_service')
 rospy.Service('/torch/detect/face_features', TorchFaceFeatureDetection, detect)
diff --git a/skills/scripts/unit_test_describe_people.py b/skills/scripts/unit_test_describe_people.py
index 3cf73d34c..44a87463e 100644
--- a/skills/scripts/unit_test_describe_people.py
+++ b/skills/scripts/unit_test_describe_people.py
@@ -16,6 +16,6 @@
 
     sm.execute()
 
-    print('\n\nDetected people:', sm.userdata['people'])
+    # print('\n\nDetected people:', sm.userdata['people'])
 
     rospy.signal_shutdown("down")
diff --git a/skills/src/lasr_skills/describe_people.py b/skills/src/lasr_skills/describe_people.py
index a6ed0256f..aa33ddf51 100644
--- a/skills/src/lasr_skills/describe_people.py
+++ b/skills/src/lasr_skills/describe_people.py
@@ -9,6 +9,7 @@
 from colour_estimation import closest_colours, RGB_COLOURS
 from lasr_vision_msgs.msg import BodyPixMaskRequest, ColourPrediction, FeatureWithColour
 from lasr_vision_msgs.srv import YoloDetection, BodyPixDetection, TorchFaceFeatureDetection
+from numpy2message import numpy2message
 
 from .vision import GetImage, ImageMsgToCv2
 
@@ -136,6 +137,40 @@ def execute(self, userdata):
                 # keep track
                 features = []
 
+                # process part masks
+                for (bodypix_mask, part) in zip(userdata.bodypix_masks, ['torso', 'head']):
+                    part_mask = np.array(bodypix_mask.mask).reshape(
+                        bodypix_mask.shape[0], bodypix_mask.shape[1])
+                    
+                    # filter out part for current person segmentation
+                    try:
+                        part_mask[mask_bin == 0] = 0
+                    except Exception:
+                        rospy.logdebug('|> Failed to check {part} is visible')
+                        continue
+
+                    if part_mask.any():
+                        rospy.logdebug(f'|> Person has {part} visible')
+                    else:
+                        rospy.logdebug(
+                            f'|> Person does not have {part} visible')
+                        continue
+
+                if part == 'torso':
+                    torso_mask = part_mask
+                elif part == 'head':
+                    head_mask = part_mask
+
+                torso_mask_data, torso_mask_shape, torso_mask_dtype = numpy2message(torso_mask)
+                head_mask_data, head_mask_shape, head_mask_dtype = numpy2message(head_mask)
+
+                full_frame = cv2_img.cv2_img_to_msg(face_region)
+                features.extend(self.torch_face_features(
+                    full_frame, 
+                    torso_mask_data, torso_mask_shape, torso_mask_dtype,
+                    head_mask_data, head_mask_shape, head_mask_dtype,
+                ).detected_features)
+
                 # process part masks
                 for (bodypix_mask, part) in zip(userdata.bodypix_masks, ['torso', 'head']):
                     part_mask = np.array(bodypix_mask.mask).reshape(