DTCWT-DCT watermarking method for multimodal biometric authentication

Bousnina, Naima; Ghouzali, Sanaa; Mikram, Mounia; Abdul, Wadood

doi:10.1145/3320326.3320409

Cited by 10 publications

(9 citation statements)

References 19 publications

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“…As shown in Figure 4, AlexNet consists of 25 layers; feature extraction is achieved through the first 23 layers, Figure 3. The histograms of an original image and its corresponding watermarked image (below) [8] whereas the feature classification is performed with the last 3 layers using the number of classes in the database. Using transfer learning in this study, only 40 nodes are included in the last fully-connected layer, which is similar to the number of users in the ORL dataset.…”

Section: B Results and Discussionmentioning

confidence: 99%

“…Then the watermarked face image is manipulated using a shuffling process before XORing it with a Hadamard code and a chaotic map to attain both the orthogonality and randomization of the protected biometric templates. In [8], the authors implemented a watermarking technique based on DTCWT-DCT to insert the fingerprint minutia in the face image. The watermarked face images are then kept in the storage.…”

Section: Related Workmentioning

confidence: 99%

“…The scheme of the watermark embedding is illustrated in Figure 1 The detailed information about this watermarking algorithm is found in [8].…”

Section: A Watermark Embeddingmentioning

confidence: 99%

“…Nevertheless, this method did not achieve high accuracy because the training dataset was very small. Other multimodal biometric watermarking works in literature such as [7], [8] have demonstrated high recognition performance. However, these methods usually rely on a multi-factor authentication technique after extracting the watermark; the authentication is attained by fusing the matching scores of the raw biometrics, which requires more processing.…”

Section: Comparisons With Related Modelsmentioning

confidence: 99%

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Multimodal Biometric Watermarking-based Transfer Learning Authentication

Ghouzali¹

2022

IJCDS

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Though biometric-based authentication systems have inherent advantages over conventional authentication systems, which use passwords and ID cards, these systems cannot ensure the security and privacy of biometric data stored in their databases. Recently, several watermarking-based algorithms have been efficiently used to protect biometric templates. However, these methods also require storing the watermarked biometrics for the matching purpose of the query during the authentication phase. The paper aims to develop an approach for authenticating watermarked biometrics using transfer learning without the need to store biometric data. Benchmark face and fingerprint databases are employed to conduct the experimentation. The obtained results validated the proposed approach's ability to discriminate between different users with a performance accuracy rate achieving 99.17% while protecting the user's biometrics.

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Section: B Results and Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…The scheme of the watermark embedding is illustrated in Figure 1 The detailed information about this watermarking algorithm is found in [8].…”

Section: A Watermark Embeddingmentioning

confidence: 99%

Section: Comparisons With Related Modelsmentioning

confidence: 99%

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Multimodal Biometric Watermarking-based Transfer Learning Authentication

Ghouzali¹

2022

IJCDS

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“…The directed tests showed the capacity to validate images with an impressively high precision level. The authors in (19) (20) presented an approach which used the face detection system as a biometric technology to mark students and employee's attendance in the organizations. The researchers in (21) and (22) presented an overview about multimodal biometrics by making use of face and ear.…”

Section: Introductionmentioning

confidence: 99%

System for Fusion of Face and Speech Modalities Using DTCWT+QFT and MFCC+RASTA Techniques

Shanthakumar¹,

Nagaraja²,

Basthikodi³

2021

IJST

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Objectives:The main objective is to propose a multimodal biometric system by forming a fusion of Face and Speech modalities using DTCWT+QFT techniques for face and MFCC+RASTA Techniques for Speech recognitions. The experimental results are compared with existing works and analysed the performance with counterparts. Methods: The proposed model, make use of DTCWT and QFT techniques to extract the features of face images and perform fusion of both. The MFCC and RASTA techniques are implemented to extract features of speech data and then fusion is applied. Various databases discussed and utilized for both face and speech recognition system proposed. Findings: The results of experimentation are compared with existing systems and analysis proved than the proposed system is placed in better position. The fusion of DTCWT and QFT techniques for face recognition system is implemented and the results using performance parameters such as False Acceptation Ratio (FAR), False Rejection Ratio (FRR), Total Success Rate (TSR), Partial Error Rate (PER), Equal Error Rate (EER) are tabulated for six different types of face data sets. The average performance of the results is compared with four existing fusion techniques and showed that the proposed system performs better. The fusion of MFCC and RASTA techniques for speech recognition system is implemented and the performance is measured by calculating accuracy, precision, recall and F1-score. These results are compared with five different schemes and proved that proposed system of fusion of face and speech traits works better for human recognitions. Novelty: Fusion of two algorithms for face recognition is implemented and the results analysed. Then the fusion of two algorithms for speech recognition is implemented and the results are analysed. The novel approach is presented to combine both face and speech recognition system in to single system to improve the security using multimodal biometrics.

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Multimodal Biometric Authentication Using Watermarking Technique

Vensila

Wesley

2022

Security, Privacy and Data Analytics

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DTCWT-DCT watermarking method for multimodal biometric authentication

Cited by 10 publications

References 19 publications

Multimodal Biometric Watermarking-based Transfer Learning Authentication

Multimodal Biometric Watermarking-based Transfer Learning Authentication

System for Fusion of Face and Speech Modalities Using DTCWT+QFT and MFCC+RASTA Techniques

Multimodal Biometric Authentication Using Watermarking Technique

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