  Due to the massive explanation of artificial intelligence, machine learning
technology is being used in various areas of our day-to-day life. In the world,
there are a lot of scenarios where a simple crime can be prevented before it
may even happen or find the person responsible for it. A face is one
distinctive feature that we have and can differentiate easily among many other
species. But not just different species, it also plays a significant role in
determining someone from the same species as us, humans. Regarding this
critical feature, a single problem occurs most often nowadays. When the camera
is pointed, it cannot detect a person's face, and it becomes a poor image. On
the other hand, where there was a robbery and a security camera installed, the
robber's identity is almost indistinguishable due to the low-quality camera.
But just making an excellent algorithm to work and detecting a face reduces the
cost of hardware, and it doesn't cost that much to focus on that area. Facial
recognition, widget control, and such can be done by detecting the face
correctly. This study aims to create and enhance a machine learning model that
correctly recognizes faces. Total 627 Data have been collected from different
Bangladeshi people's faces on four angels. In this work, CNN, Harr Cascade,
Cascaded CNN, Deep CNN & MTCNN are these five machine learning approaches
implemented to get the best accuracy of our dataset. After creating and running
the model, Multi-Task Convolutional Neural Network (MTCNN) achieved 96.2% best
model accuracy with training data rather than other machine learning models.
