Mouth Image Based Person Authentication Using DWLSTM and GRU

Recently several classification methods were introduced to solve mouth based biometric authentication systems. The results of previous investigations into mouth prints are insufficient and produce lesser authentication results. This is mainly due to the difficulties that accompany any analysis of the mouths: mouths are very flexible and pliable, and successive mouth print impressions even those obtained from the same person may significantly differ from one other. The existing machine learning methods, may not achieve higher performance and only few methods are available using deep learning for mouth biometric authentication. The use of deep learning based mouth biometrics authentication gives higher results than usual machine learning methods. The proposed mouth based biometric authentication (MBBA) system is rigorously examined with real world data and challenges with the purpose that could be expected on mouth-based solution deployed on a mobile device. The proposed system has three major steps such as (1) database collection, (2) creating model for authentication, (3) performance evaluation. The database is collected from Annamalai University deep learning laboratory which consists of 5000 video frames belongs to 10 persons. The person authentication model is created using divergence weight long short term memory (DWLSTM) and gated recurrent unit (GRU) to capture the temporal relationship in mouth images of a person. The existing and proposed methods are implemented via the Anaconda with Jupyter notebook. Finally the results of the proposed model are compared against existing methods such as support vector machine (SVM), and Probabilistic Neural Network (PNN) with respect to metrics like precision, recall, F1-score, and accuracy of mouth

Integration of biometrics and steganography: A comprehensive review

The use of an individual’s biometric characteristics to advance authentication and verification technology beyond the current dependence on passwords has been the subject of extensive research for some time. Since such physical characteristics cannot be hidden from the public eye, the security of digitised biometric data becomes paramount to avoid the risk of substitution or replay attacks. Biometric systems have readily embraced cryptography to encrypt the data extracted from the scanning of anatomical features. Significant amounts of research have also gone into the integration of biometrics with steganography to add a layer to the defence-in-depth security model, and this has the potential to augment both access control parameters and the secure transmission of sensitive biometric data. However, despite these efforts, the amalgamation of biometric and steganographic methods has failed to transition from the research lab into real-world applications. In light of this review of both academic and industry literature, we suggest that future research should focus on identifying an acceptable level steganographic embedding for biometric applications, securing exchange of steganography keys, identifying and address legal implications, and developing industry standards

Frecuencia queiloscópica en adultos en un poblado rural de Catacaos, 2023

El objetivo de la investigación fue determinar la frecuencia queiloscópica en adultos en un poblado rural de Catacaos, 2023. Esta investigación fue de tipo básica, descriptiva y transeccional; se evaluaron 302 adultos para obtener y analizar su huella labial con una lupa. Se determinó que el patrón queiloscópico más frecuente fue el tipo IV (39.6%), mientras que hubo una minoría del tipo III (4.6%); según sexo los varones (41.0%) y mujeres (38.3%) presentaron mayor frecuencia del IV, así mismo hubo menor porcentajes de los tipos III (2.9%) y II (4.9%), respectivamente. De acuerdo a los segmentos, el labio superior derecho tuvo mayor frecuencia del tipo III en varones y del tipo II en mujeres; la porción media fue del tipo IV en varones y en mujeres; y la porción izquierda fue del tipo V en varones y los tipos II y V en mujeres. En el labio inferior derecho hubo mayor frecuencia del tipo II en varones y en mujeres; la porción media fue del tipo IV en varones y en mujeres; y la porción izquierda fue del tipo II en varones y en mujeres. Concluyendo que el patrón queiloscópico más frecuente fue del tipo IV

Lip print based authentication in physical access control Environments

Abstract: In modern society, there is an ever-growing need to determine the identity of a person in many applications including computer security, financial transactions, borders, and forensics. Early automated methods of authentication relied mostly on possessions and knowledge. Notably these authentication methods such as passwords and access cards are based on properties that can be lost, stolen, forgotten, or disclosed. Fortunately, biometric recognition provides an elegant solution to these shortcomings by identifying a person based on their physiological or behaviourial characteristics. However, due to the diverse nature of biometric applications (e.g., unlocking a mobile phone to cross an international border), no biometric trait is likely to be ideal and satisfy the criteria for all applications. Therefore, it is necessary to investigate novel biometric modalities to establish the identity of individuals on occasions where techniques such as fingerprint or face recognition are unavailable. One such modality that has gained much attention in recent years which originates from forensic practices is the lip. This research study considers the use of computer vision methods to recognise different lip prints for achieving the task of identification. To determine whether the research problem of the study is valid, a literature review is conducted which helps identify the problem areas and the different computer vision methods that can be used for achieving lip print recognition. Accordingly, the study builds on these areas and proposes lip print identification experiments with varying models which identifies individuals solely based on their lip prints and provides guidelines for the implementation of the proposed system. Ultimately, the experiments encapsulate the broad categories of methods for achieving lip print identification. The implemented computer vision pipelines contain different stages including data augmentation, lip detection, pre-processing, feature extraction, feature representation and classification. Three pipelines were implemented from the proposed model which include a traditional machine learning pipeline, a deep learning-based pipeline and a deep hybridlearning based pipeline. Different metrics reported in literature are used to assess the performance of the prototype such as IoU, mAP, accuracy, precision, recall, F1 score, EER, ROC curve, PR curve, accuracy and loss curves. The first pipeline of the current study is a classical pipeline which employs a facial landmark detector (One Millisecond Face Alignment algorithm) to detect the lip, SURF for feature extraction, BoVW for feature representation and an SVM or K-NN classifier. The second pipeline makes use of the facial landmark detector and a VGG16 or ResNet50 architecture. The findings reveal that the ResNet50 is the best performing method for lip print identification for the current study. The third pipeline also employs the facial landmark detector, the ResNet50 architecture for feature extraction with an SVM classifier. The development of the experiments is validated and benchmarked to determine the extent or performance at which it can achieve lip print identification. The results of the benchmark for the prototype, indicate that the study accomplishes the objective of identifying individuals based on their lip prints using computer vision methods. The results also determine that the use of deep learning architectures such as ResNet50 yield promising results.M.Sc. (Science