A Novel Approach for Face Recognition Using
Fused GMDH-Based Networks
El-Sayed El-Alfy1, Zubair Baig2, and Radwan Abdel-Aal1
1College of Computer Sciences and Engineering, King Fahd University of Petroleum and Minerals, KSA
2School of Science and Security Research Institute, Edith
Cowan University, Australia
Abstract: This paper explores a
novel approach for automatic human recognition from multi-view frontal facial
images taken at different poses. The proposed computational model is based on
fusion of the Group Method of Data Handling (GMDH) neural networks trained on
different subsets of facial features and with different complexities. To
demonstrate the effectiveness of this approach, the performance is evaluated
and compared using eigen-decomposition for feature extraction and reduction
with a variety of GMDH-based models. The experimental results show that high
recognition rates, close to 98%, can be achieved with very low average false acceptance
rates, less than 0.12%. Performance is further investigated on different
feature set sizes and it is found that with smaller feature sets (as few as 8
features), the proposed GMDH-based models outperform other classifiers
including those using radial-basis functions and support-vector machines.
Additionally, the capability of the group method of data handling algorithm to
select the most relevant features during the model construction makes it more
attractive to build much simplified models of polynomial units.
Keywords: Face recognition, abductive machine learning, neural computing,
GMDH-based ensemble learning.
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