Face Recognition Using Computer Vision and CNN Algorithm
Objective
The main objective of this project is to recognize the faces of a person using the CNN algorithm of deep learning along with computer vision.
Abstract
Human face is the significant characteristic to identify a person. Everyone has their own unique face even for twins. Thus, a face recognition and identification are required to distinguish each other. Hence, we are proposing a method that which will able to recognize the deep learning based CNN model. Here we are collecting the dataset of different faces. Once after preprocessing it we train the data with the CNN algorithm. After training, we will test the results using the OpenCV and also can upload the image for recognition of faces.
INDEX TERMS: Convolutional neural network, Face recognition, OpenCV.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Processor-I3/Intel Processor
Ram-4GB (min)
Hard Disk-160GB
Camera-Webcam
Operating System: Windows 7/8/10
Technology: Python 3.6 +
Libraries: Tensorflow, Keras, CV2, Face_recognition
IDE: Pycharm
HARDWARE SYSTEM CONFIGURATION:
Processor-I3/Intel Processor
Ram-4GB (min)
Hard Disk-160GB
Camera-Webcam
Operating System: Windows 7/8/10
Technology: Python 3.6 +
Libraries: Tensorflow, Keras, CV2, Face_recognition
IDE: Pycharm
Learning Outcomes
- About Data Science.
- About Python programming.
- About CNN.
- About Deep learning frameworks.
- About data transmission
- About RNN, LSTM.
- About ML algorithms like RF and SVM.
- About Deep learning and Machine learning.
- Different toolboxes in Deep Learning
- Practical exposure on
- Software tools.
- Solution providing for real time problems
- Working with team/ individual
- Creative and Imagination Skills
- Work on Creative ideas
Related Projects
Paper Publishing
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Disclaimer - Takeoff Edu Group Projects, are not associated or affiliated with IEEE, in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, and not projects of or by IEEE.
