Menpo (frontal) Results

This jupyter notebook summarises the results of the active appearance models which were trained using the menpo Python Package. For more information regarding the training of these models please refer to:

Section 4 of the Review Paper
The Active Appearance Model base class contained within aam.py
The Menpo specific subclass of the base menpoAAM.py
The holisitc AAM and patch AAM models described within menpo_aam_front_hol.py and menpo_aam_front_patch.py

This notebook will contained the cumulative error distribution curves produced by the models as well as a selection from literature.

Resource Utilisation

Model	Time (hh:mm:ss)	Avg # CPUs Used	Memory Used (GB)
hAAM	06:28:44	7.83	16.6
pAAM	05:44:16	7.85	16.1

Test Set

A brief description of the test set used during the experiment. Note that the same test set was used for both holistic and patch experiments.



In [1]:

    
import os
with open('menpo_aam_front_hol_testset.txt', 'r') as f:
    test_set_filenames = f.read().split('\n')

print("The test set comprised of %i images\n" % len(test_set_filenames))
print("Listing the first 10 images:")

# Print summary
test_set_filenames = [os.path.basename(filename) for filename in test_set_filenames]
print("\t".join(test_set_filenames[:10]) + "...")









    



The test set comprised of 2004 images

Listing the first 10 images:
aflw__face_49882.jpg	aflw__face_62452.jpg	aflw__face_64405.jpg	aflw__face_43028.jpg	aflw__face_43107.jpg	aflw__face_62631.jpg	aflw__face_44199.jpg	aflw__face_42812.jpg	fddb__image2667_0.jpg	aflw__face_45491.jpg...

Cumulative Error Distribution Curves

The following chart shows the normalised error distribution as produced by the holisitic and patch AAMs vs that described by Chen et al. in 2017¹, He et al. in 2017² and Zadeh et al. in 2017³

Chen, X., Zhou, E., Mo, Y., Liu, J., & Cao, Z. (2017). Delving Deep Into Coarse-To-Fine Framework for Facial Landmark Localization. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops.
He, Z., Kan, M., Zhang, J., Chen, X., & Shan, S. (2017). A Fully End-to-End Cascaded CNN for Facial Landmark Detection. In 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017) (pp. 200–207). IEEE. http://doi.org/10.1109/FG.2017.33
Zadeh, A., Baltrusaitis, T., & Morency, L.-P. (2017). Convolutional Experts Constrained Local Model for Facial Landmark Detection. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops.



In [2]:

    
import numpy as np

# Import and configure matplotlib
import matplotlib
%matplotlib inline
matplotlib.rcParams['figure.figsize'] = (15, 10)
matplotlib.rcParams['font.size'] = 14
import matplotlib.pyplot as plt


# These files contains the CED data from experimentation & literature
FILES = ['menpo_aam_front_hol.npy', 'menpo_aam_front_patch.npy', 'menpo_front_chen_2017.npy',
         'menpo_front_he_2017.npy', 'menpo_front_zadeh_2017.npy']
LABELS = ['hAAM', 'pAAM', 'Chen et al. 2017', 'He et al. 2017',
          'Zadeh et al. 2017']

for filename, label in zip(FILES, LABELS):
    
    # Load the data
    data = np.load(filename)
    
    # Normalise
    data[1] /= np.max(data[1])
    
    # Plot
    plt.plot(data[0], data[1], label=label)


# Add details to plot
plt.legend(loc='lower right', prop={'size': 13});
plt.grid();
plt.xlabel('Point-to-point error normalised by intra-ocular distance');
plt.ylabel('Proportion of Images');