21-02-2009, 10:41 PM
Faces represent complex, multidimensional, meaningful visual stimuli
and developing a computational model for face recognition is difficult
[43]. We present a hybrid neural network solution which compares
favorably with other methods. The system combines local image sampling,
a self-organizing map neural network, and a convolutional neural
network. The self-organizing map provides a quantization of the image
samples into a topological space where inputs that are nearby in the
original space are also nearby in the output space, thereby providing
dimensionality reduction and invariance to minor changes in the image
sample, and the convolutional neural network provides for partial
invariance to translation, rotation, scale, and deformation.
The
convolutional network extracts successively larger features in a
hierarchical set of layers. We present results using the
Karhunen-Lo`eve transform in place of the self-organizing map, and a
multi-layer perceptron in place of the convolutional network. The
Karhunen-Lo`eve transform performs almost as well (5.3% error versus
3.8%). The multi-layer perceptron performs very poorly (40% error
versus 3.8%). The method is capable of rapid classification, requires
only fast, approximate normalization and preprocessing, and
consistently exhibits better classification performance than the
eigenfaces approach [43] on the database considered as the number of
images per person in the training database is varied from 1 to 5. With
5 images per person the proposed method and eigenfaces result in 3.8%
and 10.5% error respectively. The recognizer provides a measure of
confidence in its output and classification error approaches zero when
rejecting as few as 10% of the examples. We use a database of 400
images of 40 individuals which contains quite a high degree of
variability in expression, pose, and facial details. We analyze
computational complexity and discuss how new classes could be added to
the trained recognizer.
and developing a computational model for face recognition is difficult
[43]. We present a hybrid neural network solution which compares
favorably with other methods. The system combines local image sampling,
a self-organizing map neural network, and a convolutional neural
network. The self-organizing map provides a quantization of the image
samples into a topological space where inputs that are nearby in the
original space are also nearby in the output space, thereby providing
dimensionality reduction and invariance to minor changes in the image
sample, and the convolutional neural network provides for partial
invariance to translation, rotation, scale, and deformation.
The
convolutional network extracts successively larger features in a
hierarchical set of layers. We present results using the
Karhunen-Lo`eve transform in place of the self-organizing map, and a
multi-layer perceptron in place of the convolutional network. The
Karhunen-Lo`eve transform performs almost as well (5.3% error versus
3.8%). The multi-layer perceptron performs very poorly (40% error
versus 3.8%). The method is capable of rapid classification, requires
only fast, approximate normalization and preprocessing, and
consistently exhibits better classification performance than the
eigenfaces approach [43] on the database considered as the number of
images per person in the training database is varied from 1 to 5. With
5 images per person the proposed method and eigenfaces result in 3.8%
and 10.5% error respectively. The recognizer provides a measure of
confidence in its output and classification error approaches zero when
rejecting as few as 10% of the examples. We use a database of 400
images of 40 individuals which contains quite a high degree of
variability in expression, pose, and facial details. We analyze
computational complexity and discuss how new classes could be added to
the trained recognizer.