Combining shape analysis and texture pattern for palmprint identification

Mokni, Raouia; Drira, Hassen; Kherallah, Monji

doi:10.1007/s11042-016-4088-5

Cited by 20 publications

(8 citation statements)

References 38 publications

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“…Experiments were, also, conducted on another virtual database created from PolyU palmprint and ORL face databases and obtained a CIR of 99.70 per cent for 40 subjects with 4 training images. In (Mokni et al , 2016), a biometric system fusing principal line features and texture features for efficient palmprint identification is presented. A CIR of 97.98 was obtained for 230 subjects of the IITD palmprint database, with 3 training images.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Multimodal biometric system combining left and right palmprints

Taouche

Belhadef²

2019

IDD

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Purpose Palmprint recognition is a very interesting and promising area of research. Much work has already been done in this area, but much more needs to be done to make the systems more efficient. In this paper, a multimodal biometrics system based on fusion of left and right palmprints of a person is proposed to overcome limitations of unimodal systems. Design/methodology/approach Features are extracted using some proposed multi-block local descriptors in addition to MBLBP. Fusion of extracted features is done at feature level by a simple concatenation of feature vectors. Then, feature selection is performed on the resulting global feature vector using evolutionary algorithms such as genetic algorithms and backtracking search algorithm for a comparison purpose. The benefits of such step selecting the relevant features are known in the literature, such as increasing the recognition accuracy and reducing the feature set size, which results in runtime saving. In matching step, Chi-square similarity measure is used. Findings The resulting feature vector length representing a person is compact and the runtime is reduced. Originality/value Intensive experiments were done on the publicly available IITD database. Experimental results show a recognition accuracy of 99.17 which prove the effectiveness and robustness of the proposed multimodal biometrics system than other unimodal and multimodal biometrics systems.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Multimodal biometric system combining left and right palmprints

Taouche

Belhadef²

2019

IDD

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“…The karcher mean has been previously used in biometrics [67], [68]. t i is proportional to the gradient at µ k of the function V.…”

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confidence: 99%

Distances evolution analysis for online and off-line human object interaction recognition

Meng

Drira

Boonært

2018

Image and Vision Computing

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Human action recognition in 3D sequences is one of the most challenging and active areas of research in the computer vision domain. However designing automatic systems that are robust to significant variability due to object combinations and high complexity of human motions are more challenging in addition to the typical requirements such as rotation, translation, and scale invariance is challenging task. In this paper, we propose a spatio-temporal modeling of human-object interaction videos for on-line and off-line recognition. The inter joint distances and the object are considered as low-level features for online classification. For off-line recognition, we propose rate-invariant classification of full video and early recognition. A shape analysis of trajectories of the inter-joint and object-joints distances is proposed for this end. The experiments conducted following state-of-the-art settings using MSR Daily Activity 3D Dataset and Online RGBD Action Dataset and on a new Multi-view dataset for human object interaction demonstrate that the proposed approach is effective and discriminative for human object interaction classification as demonstrated here.

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“…From the first group is it possible to enumerate: Euclidean distance [30], Hamming distance [31], and average sum-squares distance [32]. e popular artificial intelligence methods are neural networks [33], support vector machine (SVM) [34], or dedicated classifiers (multiclass projection extreme learning machine (MPELM)) as proposed in [35]. Meanwhile, hand geometry was proposed as a biometric feature.…”

Section: Related Workmentioning

confidence: 99%

“…Palmprints are widely implemented in such multimodal scenarios. In [34], Mokni et al combined shape and texture in order to recognize the identity. However, shape in this approach deals with the shape of the principal lines, not with the shape of a person's hand.…”

Section: Related Workmentioning

confidence: 99%

Lightweight Verification Schema for Image-Based Palmprint Biometric Systems

Giełczyk

Choraś

Kozik

2019

Mobile Information Systems

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Palmprint biometrics is a promising modality that enables efficient human identification, also in a mobile scenario. In this paper, a novel approach to feature extraction for palmprint verification is presented. The features are extracted from hand geometry and palmprint texture and fused. The use of a fusion of features facilitates obtaining a higher accuracy and, at the same time, provides more robustness to intrusive factors like illumination, variation, or noise. The major contribution of this paper is the proposition and evaluation of a lightweight verification schema for biometric systems that improves the accuracy without increasing computational complexity which is a necessary requirement in real-life scenarios.

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Combining shape analysis and texture pattern for palmprint identification

Cited by 20 publications

References 38 publications

Multimodal biometric system combining left and right palmprints

Multimodal biometric system combining left and right palmprints

Distances evolution analysis for online and off-line human object interaction recognition

Lightweight Verification Schema for Image-Based Palmprint Biometric Systems

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