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Transformation invariant image recognition has been an active research area due to its widespread applications in a
variety of fields such as military operations, robotics, medical practices, geographic scene analysis, and many others.
The primary goal for this research is detection of objects in the presence of image transformations such as changes in
resolution, rotation, translation, scale and occlusion. We investigate a biologically-inspired neural network (NN) model
for such transformation-invariant object recognition. In a classical training-testing setup for NN, the performance is
largely dependent on the range of transformation or orientation involved in training. However, an even more serious
dilemma is that there may not be enough training data available for successful learning or even no training data at all. To
alleviate this problem, a biologically inspired reinforcement learning (RL) approach is proposed. In this paper, the RL
approach is explored for object recognition with different types of transformations such as changes in scale, size,
resolution and rotation. The RL is implemented in an adaptive critic design (ACD) framework, which approximates the
neuro-dynamic programming of an action network and a critic network, respectively. Two ACD algorithms such as
Heuristic Dynamic Programming (HDP) and Dual Heuristic dynamic Programming (DHP) are investigated to obtain
transformation invariant object recognition. The two learning algorithms are evaluated statistically using simulated
transformations in images as well as with a large-scale UMIST face database with pose variations. In the face database
authentication case, the 90° out-of-plane rotation of faces from 20 different subjects in the UMIST database is used. Our
simulations show promising results for both designs for transformation-invariant object recognition and authentication of
faces. Comparing the two algorithms, DHP outperforms HDP in learning capability, as DHP takes fewer steps to
perform a successful recognition task in general. Further, the residual critic error in DHP is generally smaller than that of
HDP, and DHP achieves a 100% success rate more frequently than HDP for individual objects/subjects. On the other
hand, HDP is more robust than the DHP as far as success rate across the database is concerned when applied in a
stochastic and uncertain environment, and the computational time involved in DHP is more.
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