Convolutional Neural Networks

Authors

Fiza Akram¹ , Rabia Riaz² , Kiran Iqbal² , Umm_e_Farwa Muhammad Anwar²

1. Riphah International University
2. Riphah International university Lahore.

Conference / event

2nd Workshop On Advancement Of Mathematics And Its Applications(WAMA-24), June 2024 (Lahore, Pakistan)

Poster summary

Convolutional Neural Networks (CNNs) are a class of deep learning algorithms primarily used for image recognition and classification. They mimic the human brain's visual processing by using layers that automatically and adaptively learn spatial hierarchies of features from input images. A typical CNN architecture includes convolutional layers, pooling layers, and fully connected layers. The convolutional layers apply filters to the input image, capturing features such as edges and textures. Pooling layers reduce the dimensionality, making the computation more efficient while preserving important information. Finally, the fully connected layers classify the images based on the learned features. CNNs are widely used in various applications, including facial recognition, medical image analysis, and self-driving cars. Their ability to learn and improve from large datasets has made them a powerful tool in the field of computer vision.

Keywords

Convolutional Neural Networks, Facial Emotion Recognation, Deep Learning, Image Processing, Computer Vision, Facial Expressions

Research areas

Mathematics, Statistics, Computer and Information Science

References

1. Albert Mehrabian. Silent Message univeristy of Calofornia Los Angeles,1971.
2. P. Ekman and W.V.Friesen University of Calofornia at San Francisco, 1983.

Funding

No data provided

Supplemental files

No data provided

Additional information

Competing interests

No competing interests were disclosed.

Data availability statement

The datasets generated during and / or analyzed during the current study are available from the corresponding author on reasonable request.

Creative Commons license

Copyright © 2024 Akram et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.