Kuldeep S Yadav
Dr. Kuldeep Singh Yadav
Scientist | Cyber Security | Affective Computing Researcher | AI Innovator | Co-Founder, NED – USUN Solar Energy
Dr. Kuldeep Singh Yadav is a Scientist at the National Aerospace Laboratories, Council of Scientific & Industrial Research (CSIR-NAL, 4PI), with a specialization in Artificial Intelligence, Deep Learning, and Computer Vision. He completed his Ph.D. in Electronics and Communication Engineering from NIT Silchar (2019–2023) and Postdoctoral Fellow from the Department of Electrical Engineering, IIT Delhi (2023-2025).
Dr. Yadav’s research focuses on real-time human computer interaction and human brain interaction systems using advanced AI/ML, and multimodal intelligence systems. He is currently working on a project, USE-Riskometer, a vision-based suspiciousness detection system integrating object detection, facial emotion recognition, and body language analysis.
He has developed several innovative approaches, including DFEAL (Deep Feature Embedding with Adaptive Learning), which combines deep feature extraction, dimensionality reduction, and traditional machine learning for biomedical and industrial signal classification.
As the non-exicutive director of USUN Solar Energy Pvt. Ltd., he also contributes to sustainable energy solutions and has led projects such as the Solar Automatic Irrigation System under government funding initiatives.
Dr. Yadav has published numerous research articles and actively mentors students and professionals in AI, computer vision, and signal processing.
Publications:
🧠 Facial Expression Recognition
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K. S. Yadav and J. Singha, “Facial expression recognition using modified Viola-John’s algorithm and KNN classifier,” Multimedia Tools and Applications, vol. 79, pp. 19379–19398, 2020.
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K. S. Yadav, J. Singha, and R. H. Laskar, “Facial expression recognition using facial features detection with fusion of classifiers: A real-time scenario,” in Proc. 4th Int. Conf. Information Systems and Computer Networks (ISCON), Mathura, India, 2019, pp. 280–285.
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S. Singh, K. S. Yadav, P. Gupta, and L. Kumar, “Cross-cultural facial expression recognition: A deep learning study on Indian cultural diversity,” in Proc. Nat. Conf. Commun. (NCC), 2025.
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K. S. Yadav, S. Singh, and L. Kumar, “FER20E: An extended facial expression recognition dataset with 20 discrete emotions,” Authorea Preprints, 2024.
✋ Gesture Recognition and Hand Interaction
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D. Sarma, H. P. J. Dutta, K. S. Yadav, M. K. Bhuyan, and R. H. Laskar, “Attention-based hand semantic segmentation and gesture recognition using deep networks,” Evolving Systems, vol. 15, pp. 211–223, 2024.
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K. S. Yadav et al., “A selective region-based detection and tracking approach towards recognition of dynamic bare hand gestures using deep neural networks,” Multimedia Systems, vol. 28, pp. 1127–1142, 2022.
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K. S. Yadav et al., “Segregation of meaningful strokes: A pre-requisite for self co-articulation removal in isolated dynamic gestures,” IET Image Processing, vol. 15, no. 7, pp. 1562–1571, 2021.
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K. S. Yadav, K. A. Monsley, and R. H. Laskar, “Gesture object detection and tracking for virtual text entry keyboard interface,” Multimedia Tools and Applications, vol. 82, pp. 19345–19362, 2023.
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K. S. Yadav et al., “Design and development of a vision-based system for detection, tracking and recognition of isolated dynamic bare hand gesticulated characters,” Expert Systems, vol. 39, no. 4, 2022.
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K. S. Yadav, A. M. Kirupakaran, and R. H. Laskar, “End-to-end bare-hand localization system for human–computer interaction: A comprehensive analysis and viable solution,” The Visual Computer, vol. 40, pp. 279–296, 2024.
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K. A. Monsley et al., “Removal of self co-articulation and recognition of dynamic hand gestures using deep architectures,” Applied Soft Computing, vol. 115, p. 108238, 2022.
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K. S. Yadav et al., “Exploration of deep learning models for localizing bare-hand in practical environments,” Eng. Appl. Artif. Intell., vol. 120, p. 105900, 2023.
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K. S. Yadav, A. M. Kirupakaran, and R. H. Laskar, “GCR-Net: A deep learning-based bare hand detection and gesticulated character recognition system for human-computer interaction,” Concurrency Computat.: Pract. Exper., vol. 35, no. 2, p. e7414, 2023.
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A. M. Kirupakaran et al., “Development of an intelligent recognition system for dynamic mid-air gesticulation of isolated alphanumeric keys,” Expert Syst. Appl., vol. 197, p. 117132, 2022.
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K. S. Yadav et al., “Detection, tracking, and recognition of isolated multi-stroke gesticulated characters,” Pattern Anal. Appl., vol. 26, pp. 543–558, 2023.
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A. M. Kirupakaran, K. S. Yadav, and R. H. Laskar, “Resolving ambiguity in recognizing case-sensitive characters gesticulated in mid-air through post-decision support modules,” in Proc. Nat. Conf. Commun. (NCC), 2022.
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A. M. Kirupakaran et al., “Design of a two-stage ASCII recognizer for case-sensitive inputs in handwritten and gesticulation mode,” Multimedia Tools and Applications, vol. 83, pp. 47911–47931, 2024.
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A. M. Kirupakaran et al., “A coherent framework for simultaneous detection and spotting of the nucleus phase from the mid-air gesticulation of alphanumeric keys,” Soft Computing, vol. 27, pp. 13591–13607, 2023.
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M. K. Anish, K. S. Yadav, and R. H. Laskar, “Self co-articulation removal in mid-air gesticulated trajectories via sequence-to-sequence LSTM-based classification,” in Proc. IEEE Delhi Sect. Conf. (DELCON), 2022, pp. 1–6.
👁️ Eye and Face Detection
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N. Ahmad et al., “An integrated approach for eye centre localization using deep networks and rectangular-intensity-gradient technique,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 9, pp. 6747–6758, 2022.
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N. Ahmad et al., “Eye detection using Faster-RCNN,” in Proc. IEEE Region 10 Symp. (TENSYMP), 2022, pp. 1–5.
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N. Ahmad et al., “Design and development of an integrated approach towards detection and tracking of iris using deep learning,” Multimedia Tools and Applications, vol. 83, pp. 24331–24350, 2024.
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N. Ahmad et al., “A cascaded deep learning framework for iris centre localization in facial images,” Expert Systems, vol. 41, no. 1, 2024.
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K. S. Yadav et al., “A holistic approach towards detection, tracking, and recognition of face,” in Proc. Int. Conf. Signal Process., Informatics, Commun. & Energy Syst., 2022, pp. 34–39.
🔠 Character and Text Recognition
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N. Saidulu et al., “Exploration of deep convolutional neural networks (via transfer learning) for handwritten character recognition,” in Proc. 2nd Int. Conf. Power, Control and Computing Technol. (ICPC2T), 2022.
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K. S. Yadav et al., “Recognition of isolated characters across different input interfaces using 2D DCNN,” in Proc. IEEE Region 10 Conf. (TENCON), 2021, pp. 1430–1435.
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N. Saidulu et al., “Investigation on inter-and intra-class ambiguity between handwritten case-sensitive characters using customized MobileNetV2,” in Proc. IEEE Region 10 Symp. (TENSYMP), 2022, pp. 243–248.
🏭 Smart Manufacturing and Predictive Maintenance
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M. Sharma et al., “Industry 4.0 technologies for smart manufacturing: A systematic review of machine learning methods for predictive maintenance,” in Proc. Int. Conf. Self Sustainable Artificial Intelligence Syst. (SUSTAIN), 2023.
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K. S. Yadav et al., “Empowering last-mile connections: A comprehensive study on emerging technological advancements,” in Proc. Int. Conf. Comput., Electron. & Electr. Eng., 2023.
🧪 Recent and Emerging Research
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P. Singh, K. S. Yadav, L. Kumar, and T. K. Gandhi, “Brain age group classification based on resting state functional connectivity metrics,” arXiv preprint arXiv:2503.12345, 2025.
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M. Aktar, K. S. Yadav, and R. H. Laskar, “A robust and lightweight generative adversarial network with zero-shot learning for image super-resolution,” in Proc. Nat. Conf. Commun. (NCC), 2025.
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K. S. Yadav, S. Singh, and L. Kumar, “Deep contextual analysis for enhanced suspiciousness estimation,” Preprint, 2024.
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