The word biometrics refers to the use of physiological or biological characteristics of human to recognize and verify the identity of an individual. Face is one of the human biometrics for passive identification with uniqueness and stability. In this manuscript we present a new face based biometric system based on neural networks supervised self organizing maps (SOM). We name our method named SOM-F. We show that the proposed SOM-F method improves the performance and robustness of recognition. We apply the proposed method to a variety of datasets and show the results.
In this manuscript we present a new multimodal biometric system based on neural networks self organizing maps (SOM) for the detection and recognition of face, ear and hand geometry. We use combined principal component analysis (PCA) and SOMs for the dimensionality reduction and then use it for the combined search space optimization of ear, face and hand geometry. We name our method named RJSOM. We show that the proposed RJSOM method improves the performance and robustness of recognition when compared to methods proposed in literature. We apply the proposed method to a variety of datasets and show the results.
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