Cascading Multimodal Verification using Face, Voice and Iris Information

Lee, Ping-Han; Chu, Lu-Jong; Hung, Yi‐Ping; Shih, Sheng-Wen; Chen, Chu-Song; Wang, Hsin‐Min

doi:10.1109/icme.2007.4284783

Cited by 9 publications

(3 citation statements)

References 8 publications

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“…However, to reveal that the facial traits can be better defined from a large set of faces collected from different resources, and compared with other algorithms in performance, a mixed dataset was used. The mixed dataset is composed of images collected from AR [5], FERET [12], FRGC [13], FVI [16], PIE [14] and XM2VTS [15]. The mixed dataset includes 6405 facial images from 903 individuals taken under uncontrolled illumination conditions, facial expressions, and poses, or using different image acquisition devices.…”

Section: Performance Evaluation-a Comparative Studymentioning

confidence: 99%

Robust Face Recognition Using Probabilistic Facial Trait Code

Lee

Hsu

et al. 2010

Computer Vision – ECCV 2010

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Recently, Facial Trait Code (FTC) was proposed for solving face recognition, and was reported with promising recognition rates. However, several simplifications in the FTC encoding make it unable to handle the most rigorous face recognition scenario in which only one facial image per individual is available for enrollment in the gallery set and the probe set includes faces under variations caused by illumination, expression, pose or misalignment. In this study, we propose the Probabilistic Facial Trait Code (PFTC) with a novel encoding scheme and a probabilistic codeword distance measure. We also proposed the Pattern-Specific Subspace Learning (PSSL) scheme that encodes and recognizes faces robustly under aforementioned variations. The proposed PFTC was evaluated and compared with state-of-the-art algorithms, including the FTC, the algorithm using sparse representation, and the one using Local Binary Pattern. Our experimental study considered factors such as the number of enrollment allowed in the gallery, the variation among gallery or probe set, and reported results for both identification and verification problems. The proposed PFTC yielded significant better recognition rates in most of the scenarios than all the states-of-the-art algorithms evaluated in this study.

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Section: Performance Evaluation-a Comparative Studymentioning

confidence: 99%

Robust Face Recognition Using Probabilistic Facial Trait Code

Lee

Hsu

et al. 2010

Computer Vision – ECCV 2010

Self Cite

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“…Multi-Factor Authentication (MFA) is a unique multi-layer approach customizable to each organization's security requirements [1], [11].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Biometric Speaker Recognition Through MFCC Feature Extraction and Polar Codes for Remote Application

Wankhede,

Wagh

2023

IEEE Access

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While extensive research has been conducted in the field of biometrics, particularly in face and fingerprint recognition, remote speaker recognition has yet to gain global acceptance due to challenges related to accuracy and data integrity. Previous studies in speaker recognition have explored techniques such as Mel Frequency Cepstral Coefficients (MFCC) and Convolutional Neural Networks (CNN), yielding accuracy rates of 90.4% and 92.8%, respectively over a fixed and small database but on a standalone system. To address the data integrity and accuracy issues for enhancement in remote speaker recognition, a novel approach is proposed in this paper. Initially, remote speaker recognition is implemented using a client-server setup, but the presence of channel noise hindered any noticeable improvement in accuracy compared to existing methods. The new approach involves extracting MFCC parameters from voice samples and subsequently applying polar errorcorrecting coding techniques for storage as well as transmission to achieve fidelity. Using a code rate of ½ and a block length of 1024 bits, the transmission of polar-coded MFCC features over a noisy channel yielded a lower bit error rate when coupled with successive list decoding. Simulation results demonstrate a reduction in bit error rate, resulting in an accuracy of 95.2% in the implemented remote speaker recognition system. This represents a significant 5% improvement over the existing standalone system that uses MFCC alone and a 2.5% enhancement over the combination of MFCC and CNN. These findings highlight that the Polar codes can be effectively utilized in speaker recognition systems to enhance their robustness and reliability, especially in scenarios with noisy channels or challenging conditions.

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“…If no single modality provides a dominant score the decision is taken according to the classifier with the highest likelihood offset amount the classifiers. Lee et al [12] proposed a fusion strategy that fuses multiple physical traits in a cascade structure, in which users are verified with individual modules sequentially in separate stages, each stage contains a unimodal module. Once the user is verified with one module the result is accepted and the rest of the modules are not processed.…”

Section: Introductionmentioning

confidence: 99%

Cascaded multimodal biometric recognition framework

et al. 2014

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A practically viable multi-biometric recognition system should not only be stable, robust and accurate but should also adhere to real-time processing speed and memory constraints. This study proposes a cascaded classifier-based framework for use in biometric recognition systems. The proposed framework utilises a set of weak classifiers to reduce the enrolled users' dataset to a small list of candidate users. This list is then used by a strong classifier set as the final stage of the cascade to formulate the decision. At each stage, the candidate list is generated by a Mahalanobis distance-based match score quality measure. One of the key features of the authors framework is that each classifier in the ensemble can be designed to use a different modality thus providing the advantages of a truly multimodal biometric recognition system. In addition, it is one of the first truly multimodal cascaded classifier-based approaches for biometric recognition. The performance of the proposed system is evaluated both for single and multimodalities to demonstrate the effectiveness of the approach.

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Cascading Multimodal Verification using Face, Voice and Iris Information

Cited by 9 publications

References 8 publications

Robust Face Recognition Using Probabilistic Facial Trait Code

Robust Face Recognition Using Probabilistic Facial Trait Code

Enhancing Biometric Speaker Recognition Through MFCC Feature Extraction and Polar Codes for Remote Application

Cascaded multimodal biometric recognition framework

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