An automated ear identification system using Gabor filter responses

Image processing techniques have been used in processing of potential fields data in order to delineate the edges of the anomalous sources. In this research, firstly the 2D Gabor filter was introduced. Then its operation was investigated on the three magnetized prisms which were at the three different depths and orientations. The effects of body wavelength, standard deviation and body orientation in the Gabor filter on the detection of edge bodies were studied. Investigations showed that the Gabor filter is a low pass filter. Afterwards, the Gabor filter was applied on the aeromagnetic data of Khoram Abad region in the southwest and ground magnetic data from Kashmar region in the northeast of Iran. The results show that the trend of geological structures is NW-SE which is in concordance to those obtained from other filters as well as geological map of the region. 1 2D Gabor filter The boundary of the geological structures can be determined by processing of potential field spectrums .Most filters which have been used for edge detection are based on the different degree of derivatives. The derivatives cause noises and signals amplify together which decrease resolution of edges. Therefore, using filters without any derivative terms is a prime objective. The Gabor filter has not any derivative terms in its mathematical structure. Denis Gabor first introduced the filter for image processing. This filter is linear and has been used for analyzing details of an image or its texture. The characteristics of the Gabor filter is to obtain special frequencies in a certain point or a region. Mostly this filter has been used for analyzing human vision system. The Gabor filter has two parts, real and imaginary in exponential forms which are multiplied with a Gaussian function. The real part has been used for data filtering. The Gabor filter response in spatial domain is defined by convolution of data in the Gabor matrix. The Gabor filter is a low pass filter which attenuates noises while it detects the edges of the deep anomalies in a certain direction. The Gabor filter in mathematics is in the group of filter transformation. On this basis, this filter can process images in different directions and frequencies. The Gabor filter has many applications in image processing such as texture analysis, fingerprint detection, edge detection, document processing and so on. The 2D Gabor filter was also used for identification of the edges of the geological structures and faults. For determination of the geological trends and faults, we must apply the Gabor filter in different directions and frequencies and standard deviations. In this article, we first introduce the Gabor filter and its characteristics. Afterwards this filter is applied on the aeromagnetic data from Khoram Abad and ground magnetic from an iron mine at Kashmar regions in the the southwest and northeast of Iran respectively. 2 The real data We prepared the following maps from the real data in Khoram Abad region: the map of the total magnetic intensity, the reduction to the pole of aeromagnetic map and the map resulted from the application of the Gabor filter on the residual aeromagnetic map of the region. From the latter map, the trend and edges of the geological structures were identified. The other trends may be detected by changing the various parameters of the filter specially its direction. The trend of the geological structures which is revealed by the Gabor filter is northwest- southeast. If the Gabor filter is applied with the other edge filters such as derivative of tilt angle, analytic signal filters, we can identify the boundaries better. The Gabor filter on the data from Kashmar region, delineates the boundary of the iron ore body at the studied area.

An ear biometric system based on artificial bees and the scale invariant feature transform

<p>Biometric verification based on ear features is modern filed for scientific research. As known, there are many biometric identifiers that can identify people such as fingerprints, iris and speech. In this paper, the focus is placed on the ear biometric model in order to verifying the identity of persons. The main idea is based on used the moments as ear feature extractors. The proposed approach included some operations as follow: image capturing, edge detection, erosion, feature extraction, and matching. The proposed approach has been tested using many images of the ears with different states. Experimental results using several trails verified that the proposed approach is achieved high accuracy level over a wide variety of ear images. Also, the verification process will be completed by matching query ear image with ear images that kept in database during real time.</p>

Biometric technology (BT) is a component of information security and person identification. Individual acceptance and adoption of BT is fundamental to successful implementation of BT by organisations. There has been a fairly moderate but improving pace of adoption of technology in developing countries. This study investigates factors affecting users' intention to use BT in a developing country based on the modified version of the unified theory of acceptance and use of technology (UTAUT). Results show that simpler biometric methods (e.g., fingerprinting) have higher level of utilisation than more complex ones (e.g., DNA). The intention to adopt biometrics is influenced by perceived ease of use, security, resource facilitating conditions, self-efficacy, and compatibility. Technology facilitating condition and awareness were found to exert some level of impact, while perceived usefulness, awareness, peer influence and complexity did not show any statistical influence on the intention to adopt BT.

The digitalization of the administration of justice requires the development of a secure and reliable digital structure for every stakeholder, including society, professionals, and administrative staff. In practice, many judicial situations require digital interaction and identity management, such as remote access to court files, personal identification during a videoconference held outside court premises, remote access to the judicial system by court officials during teleworking sessions, and the filing of pleadings. Yet, in response, digital judicial systems have not followed a uniform path in implementing tools and systems based on biometric technology. As a result, the identification systems and trust services outlined in Regulation (EU) 910/2014 (the eIDAS Regulation) have become the foundation for the advanced digitalization of justice systems in Member States. Technological progress, the entry of justice systems into more advanced stages of digitalization, and their lack of interoperability make it necessary to assess the extent to which each meets the existing legal frameworks and trustworthiness requirements of identification. This chapter aims to contribute to the identification of the regulatory requirements and practices that meet the needs of digitally advanced justice systems.

The contemporary changing technological environment forces all economic units to go through a digital transformation. The banking industry is in the epicenter of this change. Credit institutions seek to adapt a business model that is increasingly customer-oriented, providing new services with greater transparency and levels of security. The growing demand for more effective protection against fraud, phishing attacks, theft of identities and ensuring better levels of security in a timely manner is increasingly central topic in retail banking. The issue of security, as in the territory of bank branches, ATM-devices, as well as in the provision of online and mobile services, arouses the interest of credit institutions to the use of biometric identification technology. Biometric technologies are becoming an indispensable part of the means of personal verification and secure identification activities in banking institutions.

Human object detection and recognition is essential for elderly monitoring and assisted living however, models relying solely on pose or scene context often struggle in cluttered or visually ambiguous settings. To address this, we present SCENET-3D, a transformer-driven multimodal framework that unifies human-centric skeleton features with scene-object semantics for intelligent robotic vision through a three-stage pipeline. In the first stage, scene analysis, rich geometric and texture descriptors are extracted from RGB frames, including surface-normal histograms, angles between neighboring normals, Zernike moments, directional standard deviation, and Gabor-filter responses. In the second stage, scene-object analysis, non-human objects are segmented and represented using local feature descriptors and complementary surface-normal information. In the third stage, human-pose estimation, silhouettes are processed through an enhanced MoveNet to obtain 2D anatomical keypoints, which are fused with depth information and converted into RGB-based point clouds to construct pseudo-3D skeletons. Features from all three stages are fused and fed in a transformer encoder with multi-head attention to resolve visually similar activities. Experiments on UCLA (95.8%), ETRI-Activity3D (89.4%), and CAD-120 (91.2%) demonstrate that combining pseudo-3D skeletons with rich scene-object fusion significantly improves generalizable activity recognition, enabling safer elderly care, natural human–robot interaction, and robust context-aware robotic perception in real-world environments.

This paper presents a framework that uses ear images for human identification. The framework makes use of Principal Component Analysis (PCA) for ear image feature extraction and Multilayer Feed Forward Neural Network for classification. Framework are proposed to improve recognition accuracy of human identification. The framework was tested on an ear image database to evaluate its reliability and recognition accuracy. The experimental results showed that our framework achieved higher stable recognition accuracy and over-performed other existing methods. The recognition accuracy stability and computation time with respect to different image sizes and factors were investigated thoroughly as well in the experiments.

With the increasing development of internet, the security of personal information becomes more and more important. Thus, variety of personal recognition methods have been introduced to ensure persons' information security. Traditional recognition methods such as Personal Identification Number (PIN), or Identification tag (ID) are vulnerable to hackers. Then the biometric technology, which uses the unique physiological characteristics of human body to identify user information has been proposed. But the biometrics widely used at present such as human face, fingerprint, iris, and voice can also be forged and falsified. The biometric with living body features such as electromyography (EMG) signal is a good method to achieve aliveness detection and prevent the spoofing attacks. However, there are few studies on personal recognition based on EMG signal. According to the application context, personal recognition system may operate either in identification mode or verification mode. In the personal identification mode, the system recognizes an individual by searching the templates of all the users in the database for a match. While in the personal verification mode, the system validates a person's identity by comparing the captured features with her or his own template(s) stored in the system database. In this paper, both EMG-based personal identification method and EMG-based personal verification method are investigated. First, the Myo armband is placed on the right forearm (specifically, the height of the radiohumeral joint) of 21 subjects to collect the surface EMG signal under hand-open gesture. Then, two different methods are proposed for EMG-based personal identification, i.e., personal identification method based on Discrete Wavelet Transform (DWT) and ExtraTreesClassifier, and personal identification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Networks (CNN). Experiments with 21 subjects show that the identification accuracy of this two methods can achieve 99.206% and 99.203% respectively. Then based on the identification method using CWT and CNN, transfer learning algorithm is adopted to solve the model update problem when new data is added. Finally, an EMG-based personal verification method using CWT and siamese networks is proposed. Experiments show that the verification accuracy of this method can achieve 99.285%.

Personal identification using 2D ear images still has many problems such as occlusion mostly caused by hair, earrings, and clothes. To avoid this problem, we propose to divide the ear image into non-overlapping equal divisions and identify persons through these non -occluded parts separately and then combine outputs of the classification of these parts in abstract, rank, and measurement level fusion. Experimental results show that the increasing of recognition rate through combining small parts of non -occluded divisions of ear image.

Personal identification based on radar gait measurement is an important application of biometric technology because it enables remote and continuous identification of people, irrespective of the lighting conditions and subjects' outfits. This study explores an effective time-velocity distribution and its relevant parameters for Doppler-radar-based personal gait identification using deep learning. Most conventional studies on radar-based gait identification used a short-time Fourier transform (STFT), which is a general method to obtain time-velocity distribution for motion recognition using Doppler radar. However, the length of the window function that controls the time and velocity resolutions of the time-velocity image was empirically selected, and several other methods for calculating high-resolution time-velocity distributions were not considered. In this study, we compared four types of representative time-velocity distributions calculated from the Doppler-radar-received signals: STFT, wavelet transform, Wigner-Ville distribution, and smoothed pseudo-Wigner-Ville distribution. In addition, the identification accuracies of various parameter settings were also investigated. We observed that the optimally tuned STFT outperformed other high-resolution distributions, and a short length of the window function in the STFT process led to a reasonable accuracy; the best identification accuracy was 99% for the identification of twenty-five test subjects. These results indicate that STFT is the optimal time-velocity distribution for gait-based personal identification using the Doppler radar, although the time and velocity resolutions of the other methods were better than those of the STFT.

A sole biometric identifier in construction an individual identification is often not able to meet the preferred performance requirements. Biometric identification pedestal on multiple biometrics correspond to an up-and-coming trend. computerized biometric scheme for person identification gauge a “signature” of the human body, compare the resulting characteristic to a database, and cause to be an request dependent decision. These biometric systems for personal authentication and identification are based upon physiological or behavioral features which are classically characteristic, even though point in time varying, such as Face recognition, Iris recognition, Fingerprint verification, Palm print verification in making a personal identification. Multi-biometric systems, which consolidate in sequence from multiple biometric sources, are in advance popularity since they are able to conquer limitations such as non-universality, noisy sensor data, large intra-user difference and vulnerability to spoof attacks that are commonly encountered in uni-biometric systems. In this paper, it addresses the concept issues and the applications strategies of multi-biometric systems.

Feature selection is one of the best optimization problems in human recognition, which reduces the number of features, removes noise and redundant data in images, and results in high rate of recognition. This step affects on the performance of a human recognition system. This paper presents a multimodal biometric verification system based on two features of palm and ear which has emerged as one of the most extensively studied research topics that spans multiple disciplines such as pattern recognition, signal processing and computer vision. Also, we present a novel Feature selection algorithm based on Particle Swarm Optimization (PSO). PSO is a computational paradigm based on the idea of collaborative behavior inspired by the social behavior of bird flocking or fish schooling. In this method, we used from two Feature selection techniques: the Discrete Cosine Transforms (DCT) and the Discrete Wavelet Transform (DWT). The identification process can be divided into the following phases: capturing the image; pre-processing; extracting and normalizing the palm and ear images; feature extraction; matching and fusion; and finally, a decision based on PSO and GA classifiers. The system was tested on a database of 60 people (240 palm and 180 ear images). Experimental results show that the PSO-based feature selection algorithm was found to generate excellent recognition results with the minimal set of selected features.

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 propose an approach to extract features of center of foot pressure (COP) obtained by a load distribution sensor and apply this method to develop a biometrics personal identification system. Biometrics technology, as a method of personal identification, plays an important role in our daily lives. In our experiment, we have a user stand on load distribution sensor with slipper, and acquire pressure data during a simple motion, as touching a bell nearby by one hand but without movements of feet. We propose a biometrics personal identification system with less information, time and low space. First, we calculate the site of COP from the obtained pressure data. Features for identification are extracted from the position and the movement of COP. Second, we built a k-out-of-n system and a neural network (NN) model with the feature parameter. Third, we input test data to the two systems. Finally, we give a comparison of these two methods. We employ 11 volunteers. The experimental result reveals that the proposed identification method can achieve an accuracy of 12.0% in FRR (False Rejection Rate) and 1.0% in FAR (False Acceptance Rate).