Developed by @Dedeepya Yarlagadda @Siddarth Saxena
A large number of practical applications for face detection exist and contemporary work even suggests that any specialized detectors can be approximated by using fast detection classifiers. In this project, we have developed an algorithm which will detect face from the input image with less false detection rate using combined effects of computer vision concepts. This algorithm utilizes the concept of detecting edges and extracting different features from face. The result is supported by the statistics obtained from calculating the parameters defining the parts of the face. The project also implements the highly powerful concept of Support Vector Machine that is used for the classification of images into face and non-face class.
- Pycharm
- Operating System
- Face Recognition
- CV2
- Tkinter
- Numpy
- Scikit learn
Real-time images were gathered from the VNR VJIET institute's students, professors, HOD's, Dean.
A user interface was developed which recognises the face.