Identification of Missing Person Using Convolutional Neural Networks
P.D N Harsha Sai, Vanamali Vamsi Kiran, Kannuri Sai Chandra Rohith, D. Rajeswararao · 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) · 2022
There are large number of people missing in India every year. Many missing instances cannot be tracked. The current existing models are limited to regional search of missing persons. The image of person is matched using the Deep learning model, VGG16. This CNN model can be used for Face recognition in images. VGG-16 is a pre-trained model which is run on millions of images. Those weights are used in our proposed work. The NumPy module is used to represent mathematical and logical operations on arrays. This Predictive model probably identifies the missing person if the image matches and is found in the database. This methodology uses several steps which are data preprocessing, data augmentation, VGG-16, training and testing the model. Using this model, the accuracy for the training data is 90.01%. On testing, the accuracy obtained is 85.21%.