Image Enhancement and Face Identification in Surveillance Videos with Deep Learning

Project Code :TMMAAI272


The primary objective of the project titled "Image Enhancement and Face Identification in Surveillance Videos with Deep Learning" is to develop an advanced system that leverages deep learning techniques to enhance the quality of surveillance video footage and facilitate accurate face identification and recognition.


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).

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram


Software: Matlab 2020a or above


Operating Systems:

  • Windows 10
  • Windows 7 Service Pack 1
  • Windows Server 2019
  • Windows Server 2016


Minimum: Any Intel or AMD x86-64 processor

Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support


Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation

Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space


Minimum: 4 GB

Recommended: 8 GB

Learning Outcomes

·   Introduction to Matlab


·   How to start with MATLAB

·   About Matlab language

·   Matlab coding skills

·   About tools & libraries

·   Application Program Interface in Matlab

·   About Matlab desktop

·   How to use Matlab editor to create M-Files

·   Features of Matlab

·   Basics on Matlab

·   What is an Image/pixel?

·   About image formats

·   Introduction to Image Processing

·   How digital image is formed

·   Importing the image via image acquisition tools

·   Analyzing and manipulation of image.

·   Phases of image processing:

               o  Acquisition

               o  Image enhancement

               o  Image restoration

               o   Color image processing

               o  Image compression

               o   Morphological processing

               o   Segmentation etc.,

·   How to extend our work to another real time applications

·   Project development Skills

               o   Problem analyzing skills

               o   Problem solving skills

               o   Creativity and imaginary skills

               o   Programming skills

               o   Deployment

               o   Testing skills

               o   Debugging skills

               o   Project presentation skills

               o   Thesis writing skills

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