Abstract
This paper presents a new technique by support vector machines after extracting the prominent iris features using discrete wavelet transformations, to achieve an optimal classification of energy system disturbances. A framework for iris recognition and protection of the recognition system from fake iris scenes was proposed. The scale-invariant feature transform is set as an algorithm to extract local features (key points) from iris images and their classification method. To elicit the prominent iris features, the test image is first pre-processed. This will facilitate confining and segmenting the region of interest hopefully, reducing the blurring and artifacts, especially those associated with the edges. The textural features can be exploited to partition irises into regions of interest in addition to providing necessary information in the spatial distribution of intensity levels in an iris neighborhood. Next, the detection efficiency of the proposed method is achieved through extracting iris gradients and edges in complex areas and in different orientations. Further, the iris diagonal edges were easily detected after calculating the variance of different blocks in an iris and the additive noise variance in a textured image. The vertical, horizontal, and diagonal iris image gradients with different directions were successfully extracted. These gradients were extracted after adjusting the threshold amplitude obtained from the histograms of these gradients. The average calculations of MAVs, peak signal-to-noise ratios (PSNRs), and mean square errors (MSEs) within the orientation angles (?45°, +45° and 90°) for both vertical and horizontal iris gradients had occurred within the rates of 1.9455—3.1266, 36.388—39.863 dB and 0.0001—0.0026, respectively.
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