In recent years, the pandemic situation has forced the education system to shift from traditional teaching to online teaching or blended learning. The ability to monitor remote online examinations efficiently is a limiting factor to the scalability of this stage of online evaluation in the education system. Human Proctoring is the most used common approach by either asking learners to take a test in the examination centers or by monitoring visually asking learners to switch on their camera. However, these methods require huge labor, effort, infrastructure, and hardware. This paper presents an automated AI-based proctoring system- \'Attentive system\' for online evaluation by capturing the live video of the examinee. Our Attentive system includes four components to estimate the malpractices such as face detection, multiple person detection, face spoofing, and head pose estimation. Attentive Net detects the faces and draws bounding boxes along with confidences. Attentive Net also checks the alignment of the face using the rotation matrix of Affine Transformation. The face net algorithm is combined with Attentive-Net to extract landmarks and facial features. The process for identifying spoofed faces is initiated only for aligned faces by using a shallow CNN Liveness net. The head pose of the examiner is estimated by using the SolvePnp equation, to check if he/she is seeking help from others. Crime Investigation and Prevention Lab (CIPL) datasets and customized datasets with various types of malpractices are used to evaluate our proposed system. Extensive Experimental results demonstrate that our method is more accurate, reliable and robust for proctoring system that can be practically implemented in real time environment as Automated proctoring System. An improved accuracy of 0.87 is reported by authors with the combination of Attentive Net, Liveness net and head pose estimation.

Nowadays, the demand for soft-biometric-based devices is increasing rapidly because of the huge use of electronics items such as mobiles, laptops and electronic gadgets in daily life. Recently, the healthcare department also emerged with soft-biometric technology, i.e., face biometrics, because the entire data, i.e., (gender, age, face expression and spoofing) of patients, doctors and other staff in hospitals is managed and forwarded through digital systems to reduce paperwork. This concept makes the relation friendlier between the patient and doctors and makes access to medical reports and treatments easier, anywhere and at any moment of life. In this paper, we proposed a new soft-biometric-based methodology for a secure biometric system because medical information plays an essential role in our life. In the proposed model, 5-layer U-Net-based architecture is used for face detection and Alex-Net-based architecture is used for classification of facial information i.e., age, gender, facial expression and face spoofing, etc. The proposed model outperforms the other state of art methodologies. The proposed methodology is evaluated and verified on six benchmark datasets i.e., NUAA Photograph Imposter Database, CASIA, Adience, The Images of Groups Dataset (IOG), The Extended Cohn-Kanade Dataset CK+ and The Japanese Female Facial Expression (JAFFE) Dataset. The proposed model achieved an accuracy of 94.17% for spoofing, 83.26% for age, 95.31% for gender and 96.9% for facial expression. Overall, the modification made in the proposed model has given better results and it will go a long way in the future to support soft-biometric based applications.