Abstract
Detecting human faces and recognizing faces and facial expressions have always been an area of interest for different applications such as games, utilities and even security. With the advancement of machine learning, the techniques of detection and recognition have become more accurate and precise than ever before. However, machine learning remains a relatively complex field that could feel intimidating or inaccessible to many of us. Luckily, in the last couple of years, several organizations and open-source communities have been developing tools and libraries that help abstract the complex mathematical algorithms in order to encourage developers to easily create learning models and train them using any programming languages.
As part of this project, we will create a Face Detection framework in Python built on top of the work of several open-source projects and models with the hope to reduce the entry barrier for developers and to encourage them to focus more on developing innovative applications that make use of face detection and recognition.
Reference63 articles.
1. R. Rajeev et al. A fast and accurate system for face detection, identification, and verification. IEEE Transactions on Biometrics, Behavior, and Identity Science 1(2) (2019) 82-96.
2. H. Wang, J. Hu, W. Deng, Face feature extraction: a complete review, IEEE Access 6 (2017) 6001-6039.
3. M. Sharif, M.Y Javed, Javed, M. Sajjad, Face recognition based on facial features. Research Journal of Applied Sciences, Engineering and Technology 4(17) (2012) 2879-2886.
4. L. Shen, L. Bai, A review on Gabor wavelets for face recognition, Pattern analysis and applications 9 (2006) 273-292.
5. S. Anwarul, D. Susheela, A comprehensive review on face recognition methods and factors affecting facial recognition accuracy, Proceedings of ICRIC 2019: Recent Innovations in Computing (2020) 495-514.