21 September 2007 Face recognition algorithm in hyperspectral imagery by employing the K-means method and the Mahalanobis distance
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Abstract
Recently, spectral information is introduced into face recognition applications to improve the detection performance for different conditions. Besides the changes in scale, orientation, and rotation of facial images, expression, occlusion and lighting conditions change the overall appearance of faces and recognition results. To eliminate these difficulties, we introduced a new face recognition technique by using the spectral signature of facial tissues. Unlike alternate algorithms, the proposed algorithm classifies the hyperspectral imagery corresponding to each face into clusters to automatically recognize the desired face and to eliminate the user intervention in the data set. The K-means clustering algorithm is employed to accomplish the clustering and then Mahalanobis distance is computed between the clusters to identify the closest cluster in the data with respect to the reference cluster. By identifying a cluster in the data, the face that contains that cluster is identified by the proposed algorithm. Test results using real life hyperspectral imagery shows the effectiveness of the proposed algorithm.
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M. I. Elbakary, M. I. Elbakary, M. S. Alam, M. S. Alam, M. S. Aslan, M. S. Aslan, "Face recognition algorithm in hyperspectral imagery by employing the K-means method and the Mahalanobis distance", Proc. SPIE 6697, Advanced Signal Processing Algorithms, Architectures, and Implementations XVII, 669705 (21 September 2007); doi: 10.1117/12.737191; https://doi.org/10.1117/12.737191
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