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A fully fledged face recognition system consists of face detection, face alignment, and face recognition. Facial recognition has been challenging due to various unconstrained factors such as pose variation, illumination, aging, partial occlusion, low resolution, etc. The traditional approaches to face recognition have some limitations in an unconstrained environment. Therefore, the task of face recognition is improved using various deep learning architectures. Though the contemporary deep learning techniques for face recognition systems improved overall efficiency, a resilient and efficacious system is still required. Therefore, we proposed a hybrid ensemble convolutional neural network (HE-CNN) framework using ensemble transfer learning from the modified pre-trained models for face recognition. The concept of progressive training is used for training the model that significantly enhanced the recognition accuracy. The proposed modifications in the classification layers and training process generated best-in-class results and improved the recognition accuracy. Further, the suggested model is evaluated using a self-created criminal dataset to demonstrate the use of facial recognition in real-time. The suggested HE-CNN model obtained an accuracy of 99.35, 91.58, and 95% on labeled faces in the wild (LFW), cross pose LFW, and self-created datasets, respectively.

Radio telemetry is being used in India to capture the location and movements of radio-collared wild animals. Radio telemetry data analysis has changed the scenario for wildlife conservation. It has provided many models and solutions for natural habitat utilization, genetic diversity, and even implementing green corridors for the species. Insights drawn from the data collected through radio telemetry from radio-collared animals have provided impactful information to understand the wild animal’s ecology. In the past few years, India has remarkably improved the population of tigers using telemetry studies and other conservation methods. In this study, a review of the telemetry studies conducted for tigers in India in the past decade has been presented. This study summarizes the telemetry studies of tigers in India and discusses how telemetry has enhanced the knowledge of ecologists and scientists to understand tiger ecology. This study also discusses the challenges and issues of conducting telemetry studies on tigers in India. Finally, the authors address the gaps in understanding the tiger’s ecology based on these studies and outline how radio telemetry can address these issues to better understand and conserve tigers.