A Multimodal Biometric System using Global Features for Identical Twins Identification

Mohammed, Bayan Omar; Shamsuddin, Siti Mariyam

doi:10.3844/jcssp.2018.92.107

Cited by 6 publications

(7 citation statements)

References 25 publications

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“…The goal of feature extraction is to retrieve the most important aspects of an image. The iris of the eye is being extracted using the unired moment invariants (UMI) approach [11].…”

Section: Extraction Of Iris Featuresmentioning

confidence: 99%

Identification of an Individual's Iris Using Euclidean and Mahalanobis Diagrams

Wijaya

2023

CoreIT

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The purpose of this study is to compare Euclidean and Mahalanobis geometry as a means of identifying a person using their iris. Iris is the only biometric that is truly unique and is extremely difficult to perform, making it the single most important consideration in the improvement of system security. To obtain the desired results, namely preprocessing and feature extraction, various methods will be used in image processing. Methods like the Gaussian filter, the operator sobel, and thresholding will be used in the pengolahan. Utilize the United Moment Invariant method to extend the circle (UMI). For projects that use the method of comparing the strengths of FAR and GAR, a smaller FAR was obtained for the eulidean to mahalanobis ratio. Additionally, value distance mahalanobis is smaller compared to FAR for GAR penetration. Keywords: Gaussian Filter, Iris, Sobel Operator, United Moment Invariat

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“…The goal of feature extraction is to retrieve the most important aspects of an image. The iris of the eye is being extracted using the unired moment invariants (UMI) approach [11].…”

Section: Extraction Of Iris Featuresmentioning

confidence: 99%

Identification of an Individual's Iris Using Euclidean and Mahalanobis Diagrams

Wijaya

2023

CoreIT

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“…Since the extracted features are in the multirepresentations zone, then it has been used in combined form. Such combined features are termed as Dis-Eigen feature vectors in the Uni-representation zone, which is employed after the process of feature extraction [12], [28], [29].…”

Section: Extraction Of the Featurementioning

confidence: 99%

New Feature-level Algorithm for a Face-fingerprint Integral Multi-biometrics Identification System

et al. 2022

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This article delves into the power of multi-biometric fusion for individual identification. a new feature-level algorithm is proposed that is the Dis-Eigen algorithm. Here, a feature-fusion framework is proposed for attaining better accuracy when identifying individuals for multiple biometrics. The framework, therefore, underpins the new multi-biometric system as it guides multi-biometric fusion applications at the feature phase for identifying individuals. In this regard, the Face-fingerprints of 20 individuals represented by 160 images were used in this framework . Experimental resultants of the proposed approach show 93.70 % identification rate with feature-level fusion multi-biometric individual identification.

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“…Traditional action recognition technology is keen to extract solid action features from video and train the classifier for the next classification, but action recognition based on in-depth learning is to design a more reasonable end-to-end neural network and design more effective network training methods. e previous methods of action recognition can be divided into two types: recognition methods based on low-level action features and those based on high-level meaning information [11].…”

Section: Action Identification and Classificationmentioning

confidence: 99%

Human Motion Recognition Based on Multimodal Characteristics of Learning Quality in Football Scene

Gao

2021

Mathematical Problems in Engineering

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The task of human motion recognition based on video is widely concerned, and its research results have been widely used in intelligent human-computer interaction, virtual reality, intelligent monitoring, security, multimedia content analysis, etc. The purpose of this study is to explore the human action recognition in the football scene combined with learning quality related multimodal features. The method used in this study is to select BN-Inception as the underlying feature extraction network and use uncontrolled environment and real world to capture datasets UCFl01 and HMDB51, and pretraining is carried out on the ImageNet dataset. The spatial depth convolution network takes image frame as input, and the temporal depth convolution network takes stacked optical flow as input to carry out human action multimodal identification. In the results of multimodal feature fusion, the accuracy of UCFl01 dataset is generally high, all of which are over 80%, and the highest is 95.2%, while the accuracy of HMDB51 dataset is about 70%, and the lowest is only 56.3%. It can be concluded that the method of this study has higher accuracy and better effect in multimodal feature acquisition, and the accuracy of single-mode feature recognition is significantly lower than that of multimodal feature recognition. It provides an effective method for the multimodal feature of human motion recognition in the scene of football or sports.

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A Multimodal Biometric System using Global Features for Identical Twins Identification

Cited by 6 publications

References 25 publications

Identification of an Individual's Iris Using Euclidean and Mahalanobis Diagrams

Identification of an Individual's Iris Using Euclidean and Mahalanobis Diagrams

New Feature-level Algorithm for a Face-fingerprint Integral Multi-biometrics Identification System

Human Motion Recognition Based on Multimodal Characteristics of Learning Quality in Football Scene

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