Facial Recognition and Object Detection using Machine learning

Facial recognition and object detection are critical computer vision problems used in security, surveillance, autonomous systems, and human-computer interaction. This study investigates the of machine learning techniques. Use of facial recognition and object detection in deep learning has been develop to high level using machine learning. In enhancing the performance and enhancing the generalization of the model, this studies implements a recognition system on VGG16 model with data augmentation. To feed into the AI model, 1800 images were extracted from 17 classes and pre-processed, normalized and augmented also through rotation and flipping. The VGG16 model architecture was used and then trained by using categorical cross entropy loss function and Adam optimizer. The model achieved a validation accuracy of 76.39% within five epochs, signifying the model’s potential on various facial variations. Some of the issues that were pointed out include shortcoming such as misidentification and low quality images. Further researches propose to expand the dataset used for the study, to employ more complex base architectures such as ResNet, and to augment data pre-processing to improve recognition accuracy.