Machine learning was defined in 90’s by Arthur Samuel described as the,” it is a field of study that gives the ability to the computer for self-learn without being explicitly programmed”, that means imbuing knowledge to machines without hard-coding it.
“A computer algorithm/program is said to learn from performance measure P and experience E with some class of tasks T if its performance at tasks in T, as measured by P, improves with experience E.” -Tom M. Mitchell.
Machine learning is mainly focused on the development of computer programs which can teach themselves to grow and change when exposed to…
Face Recognition is a recognition technique used to detect faces of individuals whose images saved in the data set. Despite the point that other methods of identification can be more accurate, face recognition has always remained a significant focus of research because of its non-meddling nature and because it is people’s facile method of personal identification.
Face Recognition Methods:-
There are different methods for face recognition, which are as follows-
1.Geometric Based / Template Based:-
Face recognition algorithms classified as geometry based or template based algorithms. The template-based methods can be constructed using statistical tools like SVM [Support Vector Machines], PCA [Principal Component Analysis], LDA [Linear Discriminant Analysis]…
In the past few years, face recognition owned significant consideration and appreciated as one of the most promising applications in the field of image analysis. Face detection can consider a substantial part of face recognition operations. According to its strength to focus computational resources on the section of an image holding a face. The method of face detection in pictures is complicated because of variability present across human faces such as pose, expression, position and orientation, skin colour, the presence of glasses or facial hair, differences in camera gain, lighting conditions, and image resolution.
Object detection is one of the…
Divyansh Dwivedi
Data Science, Artificial Intelligence, Deep Learning, Computer Vision, Machine Learning, Data Visualization and Coffee😎
