HDL-PI: hybrid DeepLearning technique for person identification using multimodal finger print, iris and face biometric features

Jadhav, Sharad B.; Deshmukh, Nilesh; Humbe, Vikas T.

doi:10.1007/s11042-022-14241-9

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…The backward LSTM module is used to capture the past contextual information whereas forward LSTM captures the future information. The final hidden state can be expressed as (20):…”

Section: Cnn-lstm Classifier Constructionmentioning

confidence: 99%

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Deep learning based biometric authentication using electrocardiogram and iris

Kailas,

Keshava Murthy

2024

IJ-AI

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Authentication systems play an important role in wide range of applications. The traditional token certificate and password-based authentication systems are now replaced by biometric authentication systems. Generally, these authentication systems are based on the data obtained from face, iris, electrocardiogram (ECG), fingerprint and palm print. But these types of models are unimodal authentication, which suffer from accuracy and reliability issues. In this regard, multimodal biometric authentication systems have gained huge attention to develop the robust authentication systems. Moreover, the current development in deep learning schemes have proliferated to develop more robust architecture to overcome the issues of tradition machine learning based authentication systems. In this work, we have adopted ECG and iris data and trained the obtained features with the help of hybrid convolutional neural network- long short-term memory (CNN-LSTM) model. In ECG, R peak detection is considered as an important aspect for feature extraction and morphological features are extracted. Similarly, gabor-wavelet, gray level co-occurrence matrix (GLCM), gray level difference matrix (GLDM) and principal component analysis (PCA) based feature extraction methods are applied on iris data. The final feature vector is obtained from MIT-BIH and IIT Delhi Iris dataset which is trained and tested by using CNN-LSTM. The experimental analysis shows that the proposed approach achieves average accuracy, precision, and F1-core as 0.985, 0.962 and 0.975, respectively.

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“…The backward LSTM module is used to capture the past contextual information whereas forward LSTM captures the future information. The final hidden state can be expressed as (20):…”

Section: Cnn-lstm Classifier Constructionmentioning

confidence: 99%

“…Jadhav et al [20] presented a multimodal biometric authentication system by using the combination of palm-print, iris, and face Hammad et al [5] developed a combination of ECG and fingerprint-based authentication system. CNN model is used for generating the combined feature set and biometric templates are generated from these features.…”

Section: Introductionmentioning

confidence: 99%

Deep learning based biometric authentication using electrocardiogram and iris

Kailas,

Keshava Murthy

2024

IJ-AI

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“…The information accessible in one of the biometric system's modules can be utilized to combine the many qualities in multimodal biometric systems. A particularly enticing secure identi cation method, comparing multimodal and unimodal biometric systems, reveals bene ts [3]. Several biometrics researchers have employed machine learning methods for recognition.…”

Section: Introductionmentioning

confidence: 99%

Improved Human Identification by Multi-biometric Image Sensor Integration With a Deep Learning Approach

Amin,

Gantra

et al. 2024

Preprint

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Biometric identification technology has become a common part of daily life due to the global demand for information security and security legislation. Due to its capacity to circumvent several fundamental drawbacks of unimodal biometric systems, multimodal biometrics technology has attracted attention and grown in popularity in this respect. This research presents a novel multimodal biometric person identification system based on a VGG19 with softmax classifier (VGG19-SC) for iris and facial biometrics. The system's architecture is built on VGG19-SC, which extracts features from and categorizes images. The system was created by combining the iris and face portions of two VGG19-SC models. VGG-19 was employed to construct the well-known pertained model. A few methods, including picture augmentation and dropout techniques, were used to prevent overfitting. The VGG19-SC models were fused using feature-level and score-level fusion methods to investigate the effects of these fusion methods on recognition performance. The results demonstrated that three biometric features were more effective than two and one biometric traits in biometric identification systems. The findings similarly demonstrated the suggested method easily surpassed other cutting-edge approaches by obtaining an accuracy of 99.39% in a multi-biometric verification system.

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Secure deep multimodal biometric authentication using online signature and face features fusion

Singhal,

Shinghal

2023

Multimed Tools Appl

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HDL-PI: hybrid DeepLearning technique for person identification using multimodal finger print, iris and face biometric features

Cited by 5 publications

References 32 publications

Deep learning based biometric authentication using electrocardiogram and iris

Deep learning based biometric authentication using electrocardiogram and iris

Improved Human Identification by Multi-biometric Image Sensor Integration With a Deep Learning Approach

Secure deep multimodal biometric authentication using online signature and face features fusion

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