TY - JOUR ID - 122236 TI - Frontal and non-frontal face detection using deep neural networks (DNN) JO - International Journal of Research in Industrial Engineering JA - RIEJ LA - en SN - 2783-1337 AU - Prasad, N. AU - Rajpal, B. AU - R. Mangalore, K. K. AU - Shastri, R. AU - Pradeep, N. AD - Department of MCA, School of Computer Science and IT, Jain (deemed-to-be) University, Bengaluru, India. Y1 - 2021 PY - 2021 VL - 10 IS - 1 SP - 9 EP - 21 KW - Face recognition KW - Deep Neural Networks (DNN) KW - OpenCV KW - NumPy KW - PyCharm KW - Python KW - Machine Learning DO - 10.22105/riej.2021.264744.1177 N2 - Face recognition has always been one of the most searched and popular applications of object detection, starting from the early seventies. Facial recognition is used for access control, authentication, fraud detection, surveillance, and by individuals to unlock their devices. The less intrusive and robustness of the face detection systems, make it better than the fingerprint scanner and iris scanner. The frontal face can be easily detected, but multi-view face detection remains a difficult task, due to various factors like illumination, various poses, occlusions, and facial expressions. In this paper, we propose a Deep Neural Network (DNN) based approach to improve the accuracy of detection of the face. We show that Deep Neural Networks algorithms have better accuracy than traditional face detection algorithms for multi-view face detection. The Deep Neural Network (DNN) gives more precise and accurate results, as the DNN model is trained with large datasets and, the model learns the best features from the dataset. UR - https://www.riejournal.com/article_122236.html L1 - https://www.riejournal.com/article_122236_f083509815545e716ec35e8475a2c894.pdf ER -