Modified CNN Model for Classifying Gender of Thermal Images Using Cloud Computing

Alyaa Jaber Jalil, Essam Ahmed El-Seidy, Sameh Sami Daoud, Naglaa Mohammed Reda


The use of thermal images in many applications has become common, especially for night surveillance systems and thermal examination devices, and even though many details are almost hidden or unclear with regard to thermal imaging, it gave many advantages, and it became possible to determine the gender of people through these images despite the difficulty of human vision. A convolutional neural network (CNN) has been designed that differentiates a person's gender based on thermal faces. The principle of Cloud computing has been adopted as it provides an appropriate environment. Experiments were carried out on a thermal database. The proposed model gives 97.75% gender classification precision, and overall accuracy (99%).

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