diff --git a/skimage/feature/__init__.py b/skimage/feature/__init__.py
index 1f7c960b..8b33d6c7 100644
--- a/skimage/feature/__init__.py
+++ b/skimage/feature/__init__.py
@@ -12,6 +12,7 @@ from .template import match_template
 from ._brief import brief, match_keypoints_brief
 from .util import pairwise_hamming_distance
 from .censure import keypoints_censure
+from .orb import keypoints_orb, descriptor_orb
 
 __all__ = ['daisy',
            'hog',
@@ -36,4 +37,6 @@ __all__ = ['daisy',
            'structure_tensor',
            'structure_tensor_eigvals',
            'hessian_matrix',
-           'hessian_matrix_eigvals']
+           'hessian_matrix_eigvals',
+           'keypoints_orb',
+           'descriptor_orb']
diff --git a/skimage/feature/orb.py b/skimage/feature/orb.py
index 009d1276..f6b4e01c 100644
--- a/skimage/feature/orb.py
+++ b/skimage/feature/orb.py
@@ -3,6 +3,39 @@ import numpy as np
 from ..util import img_as_float
 from .util import _mask_border_keypoints
 
+from skimage.feature import corner_fast, corner_orientations, corner_peaks
+from skimage.transform import pyramid_gaussian
+
+
+def keypoints_orb(image, n=9, threshold=0.20, downscale_factor=1.414,
+                  n_scales=5):
+
+    image = np.squeeze(image)
+    if image.ndim != 2:
+        raise ValueError("Only 2-D gray-scale images supported.")
+
+    pyramid = list(pyramid_gaussian(image, n_scales - 1, downscale_factor))
+
+    ofast_mask = np.array([[0, 0, 1, 1, 1, 0, 0],
+                           [0, 1, 1, 1, 1, 1, 0],
+                           [1, 1, 1, 1, 1, 1, 1],
+                           [1, 1, 1, 1, 1, 1, 1],
+                           [1, 1, 1, 1, 1, 1, 1],
+                           [0, 1, 1, 1, 1, 1, 0],
+                           [0, 0, 1, 1, 1, 0, 0]], dtype=np.uint8)
+
+    keypoints = np.empty((0, 2), dtype=np.intp)
+    orientations = np.empty((0), dtype=np.double)
+    scales = np.empty((0), dtype=np.intp)
+
+    for i in range(n_scales):
+        corners = corner_peaks(corner_fast(pyramid[i], n, threshold), min_distance=1)
+        keypoints = np.vstack((keypoints, corners))
+        orientations = np.hstack((orientations, corner_orientations(pyramid[i], corners, ofast_mask)))
+        scales = np.hstack((scales, i * np.ones((corners.shape[0]), dtype=np.intp)))
+
+    return keypoints, orientations, scales
+
 
 def descriptor_orb(image, keypoints, keypoints_angle):