Express Prediction Of External Distinctive Features Of Person Using The Program Of Dermatoglyphics For Prediction

The aim of our study was to investigate the current state of computer identification applications, such as artificial neural networks. The material of our study were antroposcopic and anthropometric parameters obtained from 180 male and females aged 18–55 years living in the Ivano-Frankivsk region and belonging to Boiko, Lemko or Hutsul ethno-territorial group. Prints of comb pattern of the toes obtained by scanning with Futronic\u27s FS80 USB2.0 Fingerprint Scanner using the program ftrScanApiEx.exe. followed by the transfer of data to a personal computer. For statistical processing of the obtained data we use STATISTICA 12 from the company StatSoft. Construction of neural networks was carried out using Neural Networks. As a result of our research there was carried out the prediction of anthropometric and antroposcopic parameters (ethno-territorial and gender belonging, etc.) through the use of dermatoglyphic parameters of the hands and feet in 180 people living in the Ivano-Frankivsk region. The proposed method allowed to obtain the results with a forecasts probability 73–90 %. The use of above algorithm of actions allowed a 50 % increase of quality of identification of unknown person for using dermatoglyphic method and 67 % facilitatation of the process of identification (of quantitative and qualitative calculations, determining correlations between parameters) in comparison with previously known manner. Therefore, our proposed method can be used as an express diagnostics of common phenotypic traits of the person (ethno-territorial affiliation, gender, etc.) at admission of mass victims (natural disasters, acts of terrorism, armed conflicts, man-made disasters, etc.), it doesn\u27t not require a long time for conducting, specially trained staff and is inexpensive.Conclusions: The possibility of predicting external-recognizing features of a person such as etno-racial belonging, sex, anthropometric and antroposcopic parameters will allow widely use dermatoglyphic method at the level with other methods in conducting forensic identification of impersonal, fragmented and putrefactive modified corpses

System of gender identification and age estimation from radiography: a review

Under extreme conditions postmortem, dental radiography examinations can play an essential role in individual identification. In forensic odontology, individual identification traditionally compares antemortem dental records radiographs with those obtained on postmortem examination. As such, these traditional methods are vulnerable to oversights or mistakes in the individual identification of unidentified bodies. Digital technology can develop forensic odontology well. An automatic individual identification system is needed to support the forensic odontology process more easily and quickly because there are still opportunities to be created. We aimed to review the complete range of recent developments in identifying individuals from panoramic radiographs. We study methods in gender identification, age estimation, radiographic segmentation, performance analysis, and promising future directions

Fingerprint Classification Using Transfer Learning Technique

Fingerprints play a significant role in many sectors. Nowadays, fingerprints are used for identification purposes in criminal investigations. They are also used as an authentication method since they are considered more secure than passwords. Fingerprint sensors are already widely deployed in many devices, including mobile phones and smart locks. Criminals try to compromise biometric fingerprint systems by purposely altering their fingerprints or entering fake ones. Therefore, it is critical to design and develop a highly accurate fingerprint classification. However, some fingerprint datasets are small and not sufficient to train a neural network. Thus, transfer learning is utilized. A large Sokoto Coventry Fingerprint Dataset (SOCOFing), which contains 55,273 fingerprint images, was first used to train a convolutional neural network model to detect image alteration and level of alternations. The model was able to achieve an 81% of accuracy. Then, a few layers of SOCOFing model were used and adapted to train another smaller dataset, namely ATVS-FakeFingerprint Database (ATVS-FFp DB), which contains 3,168 fingerprint images. Two models were trained. The first transferring model was built to classify images into real and fake, and a remarkable classification accuracy of 99.4% was achieved. The second transferring model was used to detect if the image was fake and if the user was cooperating in the generated faked fingerprint. The model achieved a classification accuracy of 97.5%. The transfer learning technique proves to be very effective in addressing insufficient dataset issues for deep learning

Pattern mining approaches used in sensor-based biometric recognition: a review

Sensing technologies place significant interest in the use of biometrics for the recognition and assessment of individuals. Pattern mining techniques have established a critical step in the progress of sensor-based biometric systems that are capable of perceiving, recognizing and computing sensor data, being a technology that searches for the high-level information about pattern recognition from low-level sensor readings in order to construct an artificial substitute for human recognition. The design of a successful sensor-based biometric recognition system needs to pay attention to the different issues involved in processing variable data being - acquisition of biometric data from a sensor, data pre-processing, feature extraction, recognition and/or classification, clustering and validation. A significant number of approaches from image processing, pattern identification and machine learning have been used to process sensor data. This paper aims to deliver a state-of-the-art summary and present strategies for utilizing the broadly utilized pattern mining methods in order to identify the challenges as well as future research directions of sensor-based biometric systems

Enhancing child safety with accurate fingerprint identification using deep learning technology

Utilizing deep learning algorithms to differentiate the fingerprints of children can greatly enhance their safety. This advanced technology enables precise identification of individual children, facilitating improved monitoring and tracking of their activities and movements. This can effectively prevent abductions and other forms of harm, while also providing a valuable resource for law enforcement and other organizations responsible for safeguarding children. Furthermore, the use of deep learning algorithms minimizes the potential for errors and enhances the overall accuracy of fingerprint recognition. Overall, implementing this technology has immense potential to significantly improve the safety of children in various settings. Our experiments have demonstrated that deep learning significantly enhances the accuracy of fingerprint recognition for children. The model accurately classified fingerprints with an overall accuracy rate of 93%, surpassing traditional fingerprint recognition techniques by a significant margin. Additionally, it correctly identified individual children's fingerprints with an accuracy rate of 89%, showcasing its ability to distinguish between different sets of fingerprints belonging to different children

Gender Estimation from Fingerprints Using DWT and Entropy

Gender estimation from fingerprints have wide range of applications, especially in the field of forensics where identifying the gender of a criminal can reduce the list of suspects significantly. Although there have been quite a few research papers in the field of gender estimation from fingerprints most of those experiments used a lot of features but were only able to achieve poor classification results. That being the motivation behind the study we successfully proposed two different approaches for gender estimation from fingerprints and achieved high classification accuracy.;In this study we have developed two different approaches for gender estimation from fingerprints. The dataset used consists of 498 fingerprints of which 260 are male and 238 are female fingerprints. The first approach is based on wavelet analysis and uses features obtained from a six level discrete wavelet transform (DWT). Classification is performed using a decision stump classifier implemented in weka and was able to achieve a classification accuracy of 95.38% using the DWT approach. The second approach uses wavelet packet analysis and extracted the Shannon entropy and log-energy entropy from the coefficients of wavelet packet transform and provided a classification accuracy of 96.59% on the same dataset using decision stump classifier implemented in weka

An Efficient Fingerprint Identification using Neural Network and BAT Algorithm

The uniqueness, firmness, public recognition, and its minimum risk of intrusion made fingerprint is an expansively used personal authentication metrics. Fingerprint technology is a biometric technique used to distinguish persons based on their physical traits. Fingerprint based authentication schemes are becoming increasingly common and usage of these in fingerprint security schemes, made an objective to the attackers. The repute of the fingerprint image controls the sturdiness of a fingerprint authentication system. We intend for an effective method for fingerprint classification with the help of soft computing methods. The proposed classification scheme is classified into three phases. The first phase is preprocessing in which the fingerprint images are enhanced by employing median filters. After noise removal histogram equalization is achieved for augmenting the images. The second stage is the feature Extraction phase in which numerous image features such as Area, SURF, holo entropy, and SIFT features are extracted. The final phase is classification using hybrid Neural for classification of fingerprint as fake or original. The neural network is unified with BAT algorithm for optimizing the weight factor