Faculty Publications
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Item Leveraging multimodal behavioral analytics for automated job interview performance assessment and feedback(Association for Computational Linguistics (ACL), 2020) Agrawal, A.; George, R.A.; Ravi, S.S.; Kamath S․, S.; Anand Kumar, M.Behavioral cues play a significant part in human communication and cognitive perception. In most professional domains, employee recruitment policies are framed such that both professional skills and personality traits are adequately assessed. Hiring interviews are structured to evaluate expansively a potential employee’s suitability for the position - their professional qualifications, interpersonal skills, ability to perform in critical and stressful situations, in the presence of time and resource constraints, etc. Therefore, candidates need to be aware of their positive and negative attributes and be mindful of behavioral cues that might have adverse effects on their success. We propose a multimodal analytical framework that analyzes the candidate in an interview scenario and provides feedback for predefined labels such as engagement, speaking rate, eye contact, etc. We perform a comprehensive analysis that includes the interviewee’s facial expressions, speech, and prosodic information, using the video, audio, and text transcripts obtained from the recorded interview. We use these multimodal data sources to construct a composite representation, which is used for training machine learning classifiers to predict the class labels. Such analysis is then used to provide constructive feedback to the interviewee for their behavioral cues and body language. Experimental validation showed that the proposed methodology achieved promising results. © 2017 Association for Computational LinguisticsItem Multimodal behavior analysis in computer-enabled laboratories using nonverbal cues(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2020) Banerjee, S.; Ashwin, T.S.; Guddeti, R.M.R.In the modern era, there is a growing need for surveillance to ensure the safety and security of the people. Real-time object detection is crucial for many applications such as traffic monitoring, security, search and rescue, vehicle counting, and classroom monitoring. Computer-enabled laboratories are generally equipped with video surveillance cameras in the smart campus. But, from the existing literature, it is observed that the use of video surveillance data obtained from smart campus for any unobtrusive behavioral analysis is seldom performed. Though there are several works on the students’ and teachers’ behavior recognition from devices such as Kinect and handy cameras, there exists no such work which extracts the video surveillance data and predicts the behavioral patterns of both the students and the teachers in real time. Hence, in this study, we unobtrusively analyze the students’ and teachers’ behavioral patterns inside a teaching laboratory (which is considered as an indoor scenario of a smart campus). Here, we propose a deep convolution network architecture to classify and recognize an object in the indoor scenario, i.e., the teaching laboratory environment of the smart campus with modified Single-Shot MultiBox Detector approach. We used six different class labels for predicting the behavioral patterns of both the students and the teachers. We created our dataset with six different class labels for training deep learning architecture. The performance evaluation demonstrates that the proposed method performs better with an accuracy of 0.765 for classification and localization. © 2020, Springer-Verlag London Ltd., part of Springer Nature.