Study on Sift and Surf Feature Technique for Human Ear Identification System

Biometric authentication for personal identification is very popular now a days. Human ear recognition system is a new technology in this field. The change of appearance with the expression was a major problem in face biometrics but in case of ear biometrics the shape and appearance is fixed. That is why it is advantageous to use it for personal identification. In this paper, we have proposed a new approach for an automated system for human ear identification. Our proposed method consists of three stages. In the first stage, preprocessing of ear image is done for its contrast enhancement and size normalization. In the second stage, features are extracted through Haar wavelets followed by ear identification using fast normalized cross correlation in the third stage. The proposed method is applied on USTB ear image database and IIT Delhi. Experimental results show that our proposed system achieves an average accuracy of 97.2% and 95.2% on these databases respectively.

In this paper, we show the existence of human radio biometrics and present a human identification system that can discriminate individuals even through the walls in a non-line-of-sight condition. Using commodity Wi-Fi devices, the proposed system captures the channel state information (CSI) and extracts human radio biometric information from Wi-Fi signals using the time-reversal (TR) technique. By leveraging the fact that broadband wireless CSI has a significant number of multipaths, which can be altered by human body interferences, the proposed system can recognize individuals in the TR domain without line-of-sight radio. We built a prototype of the TR human identification system using standard Wi-Fi chipsets with $3 \times 3$ multi-in multi-out (MIMO) transmission. The performance of the proposed system is evaluated and validated through multiple experiments. In general, the TR human identification system achieves an accuracy of 98.78% for identifying about a dozen of individuals using a single transmitter and receiver pair. Thanks to the ubiquitousness of Wi-Fi, the proposed system shows the promise for future low-cost low-complexity reliable human identification applications based on radio biometrics.

India’s defense drone ecosystem remains largely dependent on manual control and pre-programmed routines, restricting adaptability in rapidly evolving or GPS-denied environments. In this paper, we propose Drishti-AI Drone-based Reinforcement System for Intelligent Human Tracking and Identification using AI , a fully autonomous UAV surveillance framework that tightly integrates Reinforcement Learning (RL)–based navigation with real-time Deep Learning (DL)–driven human detection. For autonomous mobility, we introduce AeroNav-PPO, a Proximal Policy Optimization (PPO) model trained in a 3D AirSim environment to enable intelligent navigation and dynamic obstacle avoidance. For perception, we present YOLOv8-HawkEye, a customized object detection model capable of identifying humans with real-time coordinate mapping and automated alert generation. Together, these modules form a closed perception-action loop that enables end-to-end autonomous operation. The AeroNav-PPO agent demonstrated an average episode reward of 347.0 with consistent patrol performance across complex terrains, while YOLOv8-HawkEye achieved a precision of 95.6%, recall of 87.8%, and mAP@0.5 of 93.8%. Our results validate Drishti-AI as a promising step toward fully autonomous, mission-ready surveillance drones for next-generation defense applications.

This paper presents a wireless distributed pyroelectric sensor system for tracking and identifying multiple humans based on their body heat radiation. This study aims to make pyroelectric sensors a low-cost alternative to infrared video sensors in thermal gait biometric applications. In this system, the sensor field of view (FOV) is specifically modulated with Fresnel lens arrays for functionality of tracking or identification, and the sensor deployment is chosen to facilitate the process of data-object-association. An Expectation-Maximization-Bayesian tracking scheme is proposed and implemented among slave, master, and host modules of a prototype system. Information fusion schemes are developed to improve the system identification performance for both individuals and multiple subjects. The fusion of thermal gait biometric information measured by multiple nodes is tested at four levels: sample, feature, score, and decision. Experimentally, the prototype system is able to simultaneously track two individuals in both follow-up and crossover scenarios with average tracking errors less than 0.5 m. The experimental results also demonstrate system's potential to be a reliable biometric system for the verification/identification of a small group of human subjects. The developed wireless distributed infrared sensor system can run as a standalone prisoner/patient monitoring system under any illumination conditions, as well as a complement for conventional video and audio human tracking and identification systems.

Brain-computer interfaces (BCIs) offer a direct pathway between neural activity and computational systems, enabling enhanced human performance. We developed a BCI system for real-time human target identification in aerial imagery (5 Hz), using Event-Related Potentials (ERPs) related to visual attention processes, specifically the N2pc and P3. Six subjects participated in two distinct Rapid Serial Visual Presentation (RSVP) paradigms: one for training/validation and the other for testing with novel environments and target appearances. Three ERP-based classification models were evaluated: N2pc, P3, and a combined N2pc and P3 approach. The combined model achieved the highest target identification in validation (AUC = 0.87) but declined on novel stimuli (AUC = 0.75). N2pc-only models maintained consistent performance across both sets (AUC = 0.77 validation, 0.75 testing), demonstrating strong generalization. Optimizing model selection and channel configuration per subject yielded an average sensitivity of 0.79 and specificity of 0.76 for target vs. non-target classification, respectively. For lateralized target detection, N2pc-based models classified Left vs. Right appearances with an AUC of 0.75 in both datasets. Target centroid prediction achieved correlation scores of 0.49 (validation) and 0.52 (testing) with the ground truth, indicating reliable spatial localization. These results validate the N2pc as a robust, generalizable neural marker for visual target detection and localization, enabling gaze-independent, multi-target discrimination, making it suitable for real-world search and rescue operations. This work provides a proof of concept for ERP-based BCI systems that maintain high performance across varied environments.

Development of biometric human identification algorithms and systems is reviewed. A crisis that prevents them from being used in large computing systems is demonstrated. Comprehensive reliable tests are considered as a way to overcome the crisis. Ways to take into account the influence various factors may have on the reliability of evaluating the practical efficiency of remote human identification algorithms and systems are addressed. A method to determine and calculate quantitative quality criteria of multimodal biometric data is proposed; the possibility of extrapolating the results of testing biometric algorithms to practical application is discussed. Quality functions and the artificiality of biometric data given as the measure of proximity to biometric data registered from an unaware recognition subject are studied.

A drop in physical activity and a deterioration in the capacity to undertake daily life activities are both connected with ageing and have negative effects on physical and mental health. An Elderly and Visually Impaired Human Activity Monitoring (EV-HAM) system that keeps tabs on a person’s routine and steps in if a change in behaviour or a crisis might greatly help an elderly person or a visually impaired. These individuals may find greater freedom with the help of an EVHAM system. As the backbone of human-centric applications like actively supported living and in-home monitoring for the elderly and visually impaired, an EVHAM system is essential. Big data-driven product design is flourishing in this age of 5G and the IoT. Recent advancements in processing power and software architectures have also contributed to the emergence and development of artificial intelligence (AI). In this context, the digital twin has emerged as a state-of-the-art technology that bridges the gap between the real and virtual worlds by evaluating data from several sensors using artificial intelligence algorithms. Although promising findings have been reported by Wi-Fi-based human activity identification techniques so far, their effectiveness is vulnerable to environmental variations. Using the environment-independent fingerprints generated from the Wi-Fi channel state information (CSI), we introduce Wi-Sense. This human activity identification system employs a Deep Hybrid convolutional neural network (DHCNN). The proposed system begins by collecting the CSI with a regular Wi-Fi Network Interface Controller. Wi-Sense uses the CSI ratio technique to lessen the effect of noise and the phase offset. The t- Distributed Stochastic Neighbor Embedding (t-SNE) is used to eliminate unnecessary data further. The data dimension is decreased, and the negative effects on the environment are eliminated in this process. The resulting spectrogram of the processed data exposes the activity’s micro-Doppler fingerprints as a function of both time and location. These spectrograms are put to use in the training of a DHCNN. Based on our findings, EVHAM can accurately identify these actions 99% of the time.

In this paper, a robust personal identification system which is adaptable for the face wearing spectacles is presented.A human face identification system based on the isodensity maps has been already presented.It has been confirmed that the three dimensional personal features are well reflected in the isodensity maps. On the other hand, when the registered image is a face without spectacles and the input image is a face with spectacles, the shapes of the input isodensity maps are changed caused by the spectacles and are different from the registered isodensity maps. Then, the identification accuracy was deteriorated. From this point of view, the spectacles are removed from input faces, and we tried to improve the the matching accuracy.Computer simulation using 50 people shows 95.2% correct discrimination accuracy.

Multiple human detection and tracking is a complicated task in real scenarios. We present in this article a robust approach for multiple human detection, tracking and identification. For human detection, an improved HOG human detector is used. In multiple human tracking, serving to inter-frame accordance, it is essential to generate distinctive signatures for each person. As global appearance description can be sensitive to illumination and intra-class variation, we therefore incorporate a local invariant feature (SIFT features) to increase robustness of matching. Both color-histogram and SIFT features are used in our approach. Experimental works demonstrate its effectiveness and robustness.

A comprehensive human trafficking identification and prediction system using machine learning techniques

ECG (Electrocardiogram) data analysis is one of the most widely used and important tools in cardiology diagnostics. In recent years the development of advanced deep learning techniques and GPU hardware have made it possible to train neural network models that attain exceptionally high levels of accuracy in complex tasks such as heart disease diagnoses and treatments. We investigate the use of ECGs as biometrics in human identification systems by implementing state-of-the-art deep learning models. We train convolutional neural network models on approximately 81k patients from the US, Germany and China. Currently, this is the largest research project on ECG identification. Our models achieved an overall accuracy of 95.69%. Furthermore, we assessed the accuracy of our ECG identification model for distinct groups of patients with particular heart conditions and combinations of such conditions. For example, we observed that the identification accuracy was the highest (99.7%) for patients with both ST changes and supraventricular tachycardia. We also found that the identification rate was the lowest for patients diagnosed with both atrial fibrillation and complete right bundle branch block (49%). We discuss the implications of these findings regarding the reidentification risks of patients based on ECG data and how seemingly anonymized ECG datasets can cause privacy concerns for the patients.

H.I. (Human Identification) system is a dramatic new system for identifying individuals (and objects) . This system which will become a new“added value”in the dentistry field, offers great conveniences to individuals, as well as to the nation and the international economy.H.I. system offers necessary informations for personal identification and emergency medical treatments at the 24 hr-stand-by information center with the presentation of the code number entered by computer manipulated laser beam on to the 20-30μ thick, 2 mm wide, stainless-steel or titanium made wafer adhered onto the tooth.This wafer can be adhered without anesthesia or tooth erasing, by the use of special, painted on with a brush in a simple five minute operation.This wafer lasts a lifetime, and even longer; for instance, providing facts to prove the deceased's ideas on internal organ transplantation, or his will. It is clear that this system will play a great part in ensuring a safe and advanced society.For the dentistry world, the H.I. wafer will produce a totally new demand for its adhesion and management, and at the same time augmenting the social role played by dentists.I have made a brief report on the H.I. system with the conviction that dentists are on their way to playing a larger role in providing safer and sounder everyday life.

Multibiometric systems are very applicable in human identification systems nowadays. However, using different biometrics may need extra acquisition sensors besides algorithm complexity. In this paper, a new multi-instance human identification system is proposed using different finger vein biometrics. In the proposed method, the multibiometric system is implemented using score level fusion. A complete investigation is done on effectiveness of different score level fusion strategies. For the proposed multi finger vein biometric system experimental results confirm that maximum score level fusion strategy outperforms other fusion methods in addition to some classical fusion algorithms in terms of EER.

Some technologies on biometric application have implemented different methods and techniques to solve various problems on face recognition solely on computer vision domain. One of them is face recognition using ultrasound transducer. The ultrasound transducer has been used to solve some problems found in face recognition using computer vision technologies where the latest method is limited to many external factors such as the light effect surrounding the object. The earlier researches have proved that ultrasound transducer could be used to detect human face. However, those researches have not been developed to identify a person through his/her face. Therefore, this research has proposed a new human face recognition system using ultrasound transducer and feature to detect and identify a person through his/her face with mapping of ultrasound travel time. This face recognition application has been designed to move horizontally (left to right) and vertically (top to down) in order to scan the whole face and to create matrix data. The matrix data in 15 × 15 has been composed from the signal reflection from the face, thus this reflection also represents the distance of the sensor and the object. Later, the matrix data were analyzed using ANN and conventional template matching to identify the person/object. The research has proved that ANN has lower accuracy than conventional template matching.

The study of efficient image representations has attracted significant interest due to the computational needs of large-scale applications. In this paper we study the performance of the recently proposed VLAD method for aggregating local image descriptors when combined with SURF features, in the domain of image search. The experiments show that when SURF features are used as local image descriptors, VLAD attains better performance compared to using SIFT features. We also study how the average number of local image descriptors extracted per image affects the performance and show that by controlling this number we are able to adjust the trade off between feature extraction time and search accuracy. Finally, we examine the retrieval performance of the proposed scheme with varying levels of distractor images.