BioID 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 holisitc AAM and patch AAM models described within bioid_aam_hol.py and bioid_aam_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	00:03:06	4.54	1.0
pAAM	00:06:12	4.70	1.1

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('bioid_aam_patch_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 457 images

Listing the first 10 images:
BioID_0566.pgm	BioID_1101.pgm	BioID_1473.pgm	BioID_1177.pgm	BioID_1009.pgm	BioID_0433.pgm	BioID_0855.pgm	BioID_1491.pgm	BioID_1032.pgm	BioID_1079.pgm...

Cumulative Error Distribution Curves

The following chart shows the normalised error distribution as produced by the holisitic and patch AAMs vs that described by Cristinacce & Cootes in 2006¹

Cristinacce, D., & Cootes, T. F. (2006). Feature Detection and Tracking with Constrained Local Models. Procedings of the British Machine Vision Conference 2006, 95.1-95.10. http://doi.org/10.5244/C.20.95



In [8]:

    
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 = ['bioid_aam_hol.npy', 'bioid_aam_patch.npy', 'bioid_cristinacce_clm.npy']
LABELS = ['hAAM', 'pAAM', 'Cristinacce & Cootes 2006']

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');