Image Enhancement and Face Identification in Surveillance Videos with Deep Learning

Surveillance systems play a crucial role in modern security, but their effectiveness largely depends on the quality of captured images and the ability to identify individuals accurately. This paper presents a method for enhancing surveillance video frames and performing face identification using Convolutional Neural Networks (CNNs). 

The proposed approach consists of two main components: image enhancement and face identification. In the image enhancement stage, we employ CNNs to improve the visual quality of surveillance video frames by reducing noise, enhancing contrast, and improving overall image clarity. 

This step is essential for ensuring that subsequent face identification processes are performed on high-quality data. In the face identification stage, we utilize a CNN-based deep learning model, which has been trained on a large dataset of labeled faces, to detect and recognize individuals in the enhanced frames. The model leverages the power of deep learning to extract meaningful facial features and match them against a database of known individuals, thereby facilitating accurate identification.

Keywords: Surveillance video, image enhancement, face identification, deep learning, Convolutional Neural Networks (CNNs).