Palm Images Designing and Reviewing Using SVM Model for Gender Detection

Abstract

This work is based on identifying a person’s gender using their palm image. In this paper the applied machine learning algorithm is called Support Vector Machine (SVM) to train a model that can recognize whether the palm is of a male or female. More than 11,000 palm images are collected and processed them by resizing, flipping, and making necessary adjustments to improve the model’s learning. The model was trained using the key features from these images and then tested to see how accurately it could make predictions. The results showed high accuracy, proving that palm-based gender detection using SVM is both efficient and smart. I also compared this technique with traditional palm reading methods to highlight how modern technology can make palm analysis more reliable, especially for practical use in fields like security, health screening, and advanced smart systems.

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