Efficient iris recognition at scale requires indexing structures that can search large galleries without a linear scan. This work evaluates 7 feature extractors, with 5 pre-trained CNNs (VGG16, ResNet50, ConvNeXt-Tiny, MobileNetV2, InceptionV3) and 2 classical descriptors (SIFT, ORB), combined with median binarization...
Maximilian Harrisson, L. D. de Araújo· Brazilian Symposium on Datab...· 0 citations
Weightless Neural Networks (WNNs) such as WiSARD learn by storing information directly in RAM-based memory structures, enabling fast training and efficient hardware implementation. However, conventional WiSARD memories store only binary activations or occurrence counts, providing no measure of confidence in the stored...
Helena A. B. Moreno, Natasha C. da Fonseca, Gustavo R. Villela et al.· Brazilian Symposium on Datab...· 0 citations
Weightless Neural Networks (WNNs) are a promising alternative for the image classification problem in an edge computing environment due to their low computational cost. However, the direct use of this model requires converting the image to grayscale to facilitate the conversion of an image into its binary representatio...
Marina S. R. P. Magalhães, L. D. de Araújo· Brazilian Symposium on Datab...· 0 citations
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