Detection of Gender in Crowds Using ResNet Model
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Abstract
The ResNet model is used in this investigation to suggest a gender detection solution for use in congested settings. Due to occlusions, varied stances, and various features, determining a person's gender in crowded surroundings may be a difficult and time-consuming job. The ResNet model, which is a deep convolutional neural network architecture, is used to solve these difficulties because of its capacity to capture detailed characteristics and its efficiency in managing deep network structures. The strategy that has been suggested entails preprocessing the input photos, sending those images through the ResNet model, and then extracting gender-related characteristics from those images. The ResNet model is made up of a number of residual blocks with skip connections, which makes it easier to learn complicated representations. After that, the learnt characteristics are input into fully linked layers, and then softmax activation is used to determine the subject's gender. The usefulness of the technique that was developed was shown by experimental findings on a large dataset, which achieved a high level of accuracy in gender determination. The use of the ResNet model helps the system to handle complicated scenarios and improves the system's ability to accurately recognize gender in situations with a large number of people. The method that has been developed has the potential to find applications in areas such as surveillance, crowd control, and the study of social behavior.
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