Fusion of Visual and Thermal Face Recognition Techniques: A ...

various features in feature spaces. Methods of feature fusion, including several features
such as edges, corners, lines, texture parameters, are computed and combined in a fused
feature map that may then be used for segmentation or detection. Features obtained after
dimension reduction such as PCA, LDA, ICA, LFA and other feature extraction methods
also can be used for a fused feature map. Decision Fusion (high level), combines
decisions coming from several experts. Methods of decision fusion include majority
voting, ranked list combination, AND fusion, OR fusion. A detailed analysis of fusion
methods can be reviewed at [39][41].
Numerous attempts have been made in the field of face recognition to fuse different
modalities. But relatively less publications are available in this area. Ben-Yacoub. et al
fused face and speech data enhances personal identification [42]. Gutta. et al [43] fused
using multiple neural networks classifiers to classify gender, ethnic origin, and pose of
human faces. They improved the overall face recognition system. Fang. et al [44] tried to
fuse local features and global features using SVM (Support Vector Machine). Chang, et
al [45] proved that multimodal recognition using both the face and the ear significantly
improved the overall performance over a single modality. Accuracy using the face, ear
and fused recognition was 70.5%, 71.6% and 90.9% respectively. Wilter. et al [46] and
authors from Equinox [34][35][36] fused visual and thermal images using the fusion of
experts algorithm and showed better performance over single modalities. Challenging
problems in visual face recognition can be solved with the use of thermal images to
improve overall performance.
Figure 1 shows a general framework for the comparison of face recognition
performance with five different cases; visual (Vi), thermal (Th), data fusion (Df), two
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