Faculty Publications
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Item Face Recognition (FR) systems are increasingly gaining more importance. Face detection and tracking in a complex scene forms the first step in building a practical FR system. In this paper, a method to detect and track human faces in color image sequences is described. Skin color classification and morphological segmentation is used to detect face(s) in the first frame. These detected faces are tracked over subsequent frames by using the position of the faces in the first frame as the marker and detecting for skin in the localized region. Specific advantages of this approach are that skin color analysis method is simple and powerful, and the system can be used to detect/track multiple faces. © 2002 Taylor & Francis Group, LLC.(Human face detection and tracking using skin color modeling and connected component operators) Kuchi, P.; Gabbur, P.; Subbanna Bhat, P.; Sumam David, S.2002Item Recognition of emotions from video using acoustic and facial features(Springer-Verlag London Ltd, 2015) Sreenivasa Rao, K.S.; Koolagudi, S.In this paper, acoustic and facial features extracted from video are explored for recognizing emotions. The temporal variation of gray values of the pixels within eye and mouth regions is used as a feature to capture the emotion-specific knowledge from the facial expressions. Acoustic features representing spectral and prosodic information are explored for recognizing emotions from the speech signal. Autoassociative neural network models are used to capture the emotion-specific information from acoustic and facial features. The basic objective of this work is to examine the capability of the proposed acoustic and facial features in view of capturing the emotion-specific information. Further, the correlations among the feature sets are analyzed by combining the evidences at different levels. The performance of the emotion recognition system developed using acoustic and facial features is observed to be 85.71 and 88.14 %, respectively. It has been observed that combining the evidences of models developed using acoustic and facial features improved the recognition performance to 93.62 %. The performance of the emotion recognition systems developed using neural network models is compared with hidden Markov models, Gaussian mixture models and support vector machine models. The proposed features and models are evaluated on real-life emotional database, Interactive Emotional Dyadic Motion Capture database, which was recently collected at University of Southern California. © 2013, Springer-Verlag London.Item Students’ affective content analysis in smart classroom environment using deep learning techniques(Springer New York LLC barbara.b.bertram@gsk.com, 2019) Gupta, S.K.; Ashwin, T.S.; Guddeti, R.M.R.In the era of the smart classroom environment, students’ affective content analysis plays a vital role as it helps to foster the affective states that are beneficial to learning. Some techniques target to improve the learning rate using the students’ affective content analysis in the classroom. In this paper, a novel max margin face detection based method for students’ affective content analysis using their facial expressions is proposed. The affective content analysis includes analyzing four different moods of students’, namely: High Positive Affect, Low Positive Affect, High Negative Affect, and Low Negative Affect. Engagement scores have been calculated based upon the four moods of students as predicted by the proposed method. Further, the classroom engagement analysis is performed by considering the entire classroom as one group and the corresponding group engagement score. Expert feedback and analyzed affect content videos are used as feedback to the faculty member to improve the teaching strategy and hence improving the students’ learning rate. The proposed smart classroom system was tested for more than 100 students of four different Information Technology courses and the corresponding faculty members at National Institute of Technology Karnataka Surathkal, Mangalore, India. The experimental results demonstrate the train and test accuracy of 90.67% and 87.65%, respectively for mood classification. Furthermore, an analysis was performed over incidence, distribution and temporal dynamics of students’ affective states and promising results were obtained. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.Item An empirical study of the impact of masks on face recognition(Elsevier Ltd, 2022) Jeevan, G.; Zacharias, G.C.; Nair, M.S.; Rajan, J.Face recognition has a wide range of applications like video surveillance, security, access control, etc. Over the past decade, the field of face recognition has matured and grown at par with the latest advancements in technology, particularly deep learning. Convolution Neural Networks have surpassed human accuracy in Face Recognition on popular evaluation tests such as LFW. However, most existing models evaluate their performance with an assumption of the availability of full facial information. The COVID-19 pandemic has laid forth challenges to this assumption, and to the performance of existing methods and leading-edge algorithms in the field of face recognition. This is in the wake of an explosive increase in the number of people wearing face masks. The reduced amount of facial information available to a recognition system from a masked face impacts their discrimination ability. In this context, we design and conduct a series of experiments comparing the masked face recognition performances of CNN architectures available in literature and exploring possible alterations in loss functions, architectures, and training methods that can enable existing methods to fully extract and leverage the limited facial information available in a masked face. We evaluate existing CNN-based face recognition systems for their performance against datasets composed entirely of masked faces, in contrast to the existing standard evaluations where masked or occluded faces are a rare occurrence. The study also presents evidence denoting an increased impact of network depth on performance compared to standard face recognition. Our observations indicate that substantial performance gains can be achieved by the introduction of masked faces in the training set. The study also inferred that various parameter settings determined suitable for standard face recognition are not ideal for masked face recognition. Through empirical analysis we derived new value recommendations for these parameters and settings. © 2021 Elsevier LtdItem A comprehensive review of facial expression recognition techniques(Springer Science and Business Media Deutschland GmbH, 2023) Rashmi Adyapady, R.R.; Annappa, B.Emotion recognition has opened up many challenges, which lead to various advances in computer vision and artificial intelligence. The rapid development in this field has encouraged the development of an automatic system that could accurately analyze and measure the emotions of human beings via facial expressions. This study mainly focuses on facial expression recognition from visual cues, as visual information is the most prominent channel for social communication. The paper provides a comprehensive review of recent advancements in algorithm development, presents the overall findings performed over the past decades, discusses their advantages and constraints. It explores the transition from the laboratory-controlled environment to challenging real-world (in-the-wild) conditions, focusing on essential issues that require further exploration. Finally, relevant opportunities in this field, challenges, and future directions mentioned in this paper assist the researchers and academicians in designing efficient and robust facial expression recognition systems. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.Item An ensemble approach using a frequency-based and stacking classifiers for effective facial expression recognition(Springer, 2023) Adyapady R, R.; Annappa, B.Facial Expression Recognition is an essential aspect of human behavior to communicate effectively. A more profound understanding of human behavior, accurate analysis, and interpretation of the emotional content is essential. Hence, facial features play a crucial role as they contain beneficial information about facial expressions. A baseline architecture belonging to the EfficientNet family of models is explored for feature extraction. In this work, two novel strategies, the ensemble model using the frequency-based voting approach (FV-EffNet) and the stacking classifier (SC-EffNet), are proposed to enhance classification results’ performance. The proposed system deals with both profile and frontal pose variations. The combination of deep learning models with a stacking classifier gave the best results of 98.35% and 98.06%, and the frequency-based approach used with the ensemble classifier achieved superior performance of 98.71% and 98.56% on Oulu-CASIA and RaFD datasets, respectively. The experiment results with the proposed methodology showed better performance than previous studies on Oulu-CASIA and RaFD datasets, making it more robust to pose variations. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Item A Note on “Secure and Efficient Outsourcing of PCA-Based Face Recognition”(Institute of Electrical and Electronics Engineers Inc., 2025) Rath, S.; Ramalingam, J.; Seal, S.Zhang et al. (2020) exhibit a fundamental mathematical flaw that renders their algorithm infeasible. Additionally, existing outsourcing protocols for PCA-based face recognition suffer from inadequate verification methods, undermining the reliability of these algorithms. © 2005-2012 IEEE.