A Multi Stage Approach for Object and Face Detection using CNN

Abstract

Object and face detection are important tasks in computer vision that have numerous applications, such as surveillance, image recognition, and autonomous driving. Artificial intelligence (AI) has transformed the field of image recognition, enabling machines to interpret and analyze visual data with remarkable accuracy and speed. AI algorithms use deep learning techniques to automatically recognize patterns, shapes, and features within images, allowing them to identify objects, people, and even emotions. Image recognition has numerous practical applications, from facial recognition in security systems to medical imaging for diagnosis. The approach for object detection, face recognition, and celebrity identification proposed in this research uses algorithms such the Convolution Neural Network (CNN), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), and K-Nearest Neighbor (KNN). The CNN model is proven to be more accurate than other models due to their ability to learn features from images. The multi-stage approaches for object and face detection using CNNs have shown to be effective in achieving high accuracy of 93.2% and real-time performance