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The increasing number of online courses and programs available worldwide has elevated the importance of reliable online exam proctoring. The typical proctoring process relies on webcam surveillance. However, this traditional method of proctoring is vulnerable to numerous types of face occlusions used for religious reasons or otherwise. We present a robust biometric authentication framework that combines advanced eye and face recognition, resulting in a much better live proctoring system that is further augmented by including fingerprinting. We have used cutting-edge deep learning techniques, specifically the Siamese network for fingerprint analysis and a ResNet-based eye recognition model tested with and without Gabor filters. Furthermore, our system offers much better performance compared to previously existing models. Notably, our system maintains high accuracy, approximately 98.04% for custom eye recognition, 99.01% for publicly available labeled faces dataset, 82% for niqab dataset and 87.04% for publicly available fingerprint dataset. Moreover, our model demonstrates a 10–20% improvement in face recognition under occlusion. Our solution is highly effective for not only online proctoring but also allows use in other similar situations, such as employee authentication for remote presence verification. Our system supports scenarios involving partial occlusions, such as masks and sunglasses, to full occlusions with veils, without requiring any additional hardware. 

Abstract— Recent advances show the wide-ranging applications of machine learning for solving multi-disciplinary problems in cancer cell growth detection, modeling cancer growths and treatments, etc. There is growing interests among the faculty and students at Clayton State University to study the applications of machine learning for medical imaging and propose new algorithms based on a recently funded NSF grant proposal in medical imaging, skin cancer detection, and associated smartphone apps and a web-based user-friendly diagnosis interface. We tested many available open-source ML algorithm-based software sets in Python as applied to medical image data processing, and modeling used to predict cancer growths and treatments. We study the use of ML concepts that promote efficient, accurate, secure computation over medical images, identifying and classifying cancer cells, and modeling the cancer cell growths. In this collaborative project with another university, we follow a holistic approach to data analysis leading to more efficient cancer detection based upon both cell analysis and image recognition. Here, we compare ML based software methods and analyze their detection accuracy. In addition, we acquire publicly available data of cancer cell image files and analyze using deep learning algorithms to detect benign and suspicious image samples. We apply the current pattern matching algorithms and study the available data with possible diagnosis of cancer types. 

Recent advances show the wide-ranging applications of machine learning for solving multi-disciplinary problems in cancer cell growth detection, modeling cancer growths and treatments, etc. There is growing interests among the faculty and students at Clayton State University to study the applications of machine learning for medical imaging and propose new algorithms based on a recently funded NSF grant proposal in medical imaging, skin cancer detection, and associated smartphone apps and a web-based user-friendly diagnosis interface. We tested many available open-source ML algorithm-based software sets in Python as applied to medical image data processing, and modeling used to predict cancer growths and treatments. We study the use of ML concepts that promote efficient, accurate, secure computation over medical images, identifying and classifying cancer cells, and modeling the cancer cell growths. In this collaborative project with another university, we follow a holistic approach to data analysis leading to more efficient cancer detection based upon both cell analysis and image recognition. Here, we compare ML based software methods and analyze their detection accuracy. In addition, we acquire publicly available data of cancer cell image files and analyze using deep learning algorithms to detect benign and suspicious image samples. We apply the current pattern matching algorithms and study the available data with possible diagnosis of cancer types.