Fused intra-bimodal face verification approach based on Scale-Invariant Feature Transform and a vocabulary tree

Travieso, Carlos M.; Pozo-Baños, Marcos del; Alonso, Jesús B.

doi:10.1016/j.patrec.2013.08.016

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…Advantages of multi-biometric systems. multiple templates of the same biometric method obtained with the help of a single sensor, and (e) a multimodal system combining information about the biometric features of the individual to establish his identity [2][3][4].…”

Section: Resistance On Spoof Attacksmentioning

confidence: 99%

Multimodal Biometrics for Person Authentication

Choraś¹

2020

Security and Privacy From a Legal, Ethical, and Technical Perspective

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Unimodal biometric systems have limited effectiveness in identifying people, mainly due to their susceptibility to changes in individual biometric features and presentation attacks. The identification of people using multimodal biometric systems attracts the attention of researchers due to their advantages, such as greater recognition efficiency and greater security compared to the unimodal biometric system. To break into the biometric multimodal system, the intruder would have to break into more than one unimodal biometric system. In multimodal biometric systems: The availability of many features means that the multimodal system becomes more reliable. A multimodal biometric system increases security and ensures confidentiality of user data. A multimodal biometric system realizes the merger of decisions taken under individual modalities. If one of the modalities is eliminated, the system can still ensure security, using the remaining. Multimodal systems provide information on the "liveness" of the sample being introduced. In a multimodal system, a fusion of feature vectors and/or decisions developed by each subsystem is carried out, and then the final decision on identification is made on the basis of the vector of features thus obtained. In this chapter, we consider a multimodal biometric system that uses three modalities: dorsal vein, palm print, and periocular.

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Section: Resistance On Spoof Attacksmentioning

confidence: 99%

Multimodal Biometrics for Person Authentication

Choraś¹

2020

Security and Privacy From a Legal, Ethical, and Technical Perspective

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“…The SIFT features are invariant to image scale and rotation and robust to moderate perspective transformations, addition of noise and change in illumination (Lowe, 2004;Li et al, 2009;Zhong et al, 2015). Owing to these advantages, the SIFT algorithm has been successfully used in face verification (Travieso et al, 2014), object detection (Dou and Li, 2013), image stitching (Kwon and Ha, 2010), image forgery detection (Hashmi et al, 2014), etc. However, few studies have introduced the SIFT algorithm to the field of fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis for rolling bearing based on SIFT-KPCA and SVM

Cheng

Yuan

et al. 2017

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Purpose The purpose of this paper is to propose a fault diagnosis method for rolling bearings, in which the fault feature extraction is realized in a two-dimensional domain using scale invariant feature transform (SIFT) algorithm. This method is different from those methods extracting fault feature directly from the traditional one-dimensional domain. Design/methodology/approach The vibration signal of rolling bearings is first transformed into a two-dimensional image. Then, the SIFT algorithm is applied to the image to extract the scale invariant feature vector which is highly distinctive and insensitive to noises and working condition variation. As the extracted feature vector is high-dimensional, kernel principal component analysis (KPCA) algorithm is utilized to reduce the dimension of the feature vector, and singular value decomposition technique is used to extract the singular values of the reduced feature vector. Finally, these singular values are introduced into a support vector machine (SVM) classifier to realize fault classification. Findings The experiment results show a high fault classification accuracy based on the proposed method. Originality/value The proposed approach for rolling bearing fault diagnosis based on SIFT-KPCA and SVM is highly effective in the experiment. The practical value in engineering application of this method can be researched in the future.

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“…However, in many applications, it is hard to find the ideal features. In our system the visual features are the key points with SIFT (Scale Invariant Feature Transform) descriptors [40][41][42][43], or image patches (small sub images) [44][45][46][47]. One effective statistical image presentation of VM model is Proximity Distribution (PD) [48,49].…”

Section: Introductionmentioning

confidence: 99%

Ambiguous Proximity Distribution

Wang

2014

Intelligent Computing Methodologies

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Abstract. Proximity Distribution Kernel is an effective method for bag-offeatues based image representation. In this paper, we investigate the soft assignment of visual words to image features for proximity distribution. Visual word contribution function is proposed to model ambiguous proximity distributions. Three ambiguous proximity distributions is developed by three ambiguous contribution functions. The experiments are conducted on both classification and retrieval of medical image data sets. The results show that the performance of the proposed methods, Proximity Distribution Kernel (PDK), is better or comparable to the state-of-the-art bag-of-features based image representation methods.

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Fused intra-bimodal face verification approach based on Scale-Invariant Feature Transform and a vocabulary tree

Cited by 7 publications

References 28 publications

Multimodal Biometrics for Person Authentication

Multimodal Biometrics for Person Authentication

Fault diagnosis for rolling bearing based on SIFT-KPCA and SVM

Ambiguous Proximity Distribution

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