Personal recognition using finger knuckle shape oriented features and texture analysis

Usha, K.; Ezhilarasan, M.

doi:10.1016/j.jksuci.2015.02.004

Cited by 19 publications

(10 citation statements)

References 29 publications

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“…The papers (Ahmada et al , 2016; Charfi et al , 2016; Raghavendra, 2013) discusses the multimodal system with the palmprint and the other images. The papers (Kan et al , 2015; Ushaa and Ezhilarasanb, 2016) discuss the multimodal system with finger knuckle and the other images.…”

Section: Motivationmentioning

confidence: 99%

“…However, in this technique, the recognition of finger veins is poor. Ushaa and Ezhilarasanb (2016) have proposed the multimodal recognition system based on personal recognition using finger knuckle print images. This model used the curvelet transform for feature extraction purposes.…”

Section: Motivationmentioning

confidence: 99%

“…The finger knuckle images have a small area than the palmprint images. Besides, the finger knuckle images have the unique features required for the classification of the person’s identity (Ushaa and Ezhilarasanb, 2016; Penga et al , 2014; Le-qing and San-yuan, 2010; Zhao et al , 2015; Saini and Sinha, 2015). Various kinds of meta-heuristic algorithms are used to handle optimization problems.…”

Section: Introductionmentioning

confidence: 99%

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HE-Co-HOG and k-SVM classifier for finger knuckle and palm print-based multimodal biometric recognition

Veluchamy¹,

Karlmarx

2020

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Purpose Biometric identification system has become emerging research field because of its wide applications in the fields of security. This study (multimodal system) aims to find more applications than the unimodal system because of their high user acceptance value, better recognition accuracy and low-cost sensors. The biometric identification using the finger knuckle and the palmprint finds more application than other features because of its unique features. Design/methodology/approach The proposed model performs the user authentication through the extracted features from both the palmprint and the finger knuckle images. The two major processes in the proposed system are feature extraction and classification. The proposed model extracts the features from the palmprint and the finger knuckle with the proposed HE-Co-HOG model after the pre-processing. The proposed HE-Co-HOG model finds the Palmprint HE-Co-HOG vector and the finger knuckle HE-Co-HOG vector. These features from both the palmprint and the finger knuckle are combined with the optimal weight score from the fractional firefly (FFF) algorithm. The layered k-SVM classifier classifies each person's identity from the fused vector. Findings Two standard data sets with the palmprint and the finger knuckle images were used for the simulation. The simulation results were analyzed in two ways. In the first method, the bin sizes of the HE-Co-HOG vector were varied for the various training of the data set. In the second method, the performance of the proposed model was compared with the existing models for the different training size of the data set. From the simulation results, the proposed model has achieved a maximum accuracy of 0.95 and the lowest false acceptance rate and false rejection rate with a value of 0.1. Originality/value In this paper, the multimodal biometric recognition system based on the proposed HE-Co-HOG with the k-SVM and the FFF is developed. The proposed model uses the palmprint and the finger knuckle images as the biometrics. The development of the proposed HE-Co-HOG vector is done by modifying the Co-HOG with the holoentropy weights.

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Section: Motivationmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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HE-Co-HOG and k-SVM classifier for finger knuckle and palm print-based multimodal biometric recognition

Veluchamy¹,

Karlmarx

2020

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“…To the best of our knowledge, the only approach found in the literature related to identifying persons from their hands based on one part of the hand, is the type of work that is based on identifying persons from their finger knuckle alone. A recent example of such an approach is the work of (Usha and Ezhilarasan 2015), who proposed a new method based on geometric and texture analyses of the finger knuckle.…”

Section: Literature Reviewmentioning

confidence: 99%

Victory sign biometrie for terrorists identification: Preliminary results

Hassanat

Btoush

Abbadi

et al. 2017

2017 8th International Conference on Information and Communication Systems (ICICS)

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A B S T R A C TCovering the face and all body parts, sometimes the only evidence to identify a person is their hand geometry, and not the whole hand-only two fingers (the index and the middle fingers) while showing the victory sign, as seen in many terrorists videos. This paper investigates for the first time a new way to identify persons, particularly (terrorists) from their victory sign. We have created a new database in this regard using a mobile phone camera, imaging the victory signs of 50 different persons over two sessions. Simple measurements for the fingers, in addition to the Hu Moments for the areas of the fingers were used to extract the geometric features of the shown part of the hand shown after segmentation. The experimental results using the KNN classifier were encouraging for most of the recorded persons; with about 40% to 93% total identification accuracy, depending on the features, distance metric and K used.

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“…Sonawane and Dhanokar [16] investigated major and minor knuckle surfaces imaging for personal authentication. In turn, Usha and Ezhilarasan [17] proposed a method for personal recognition using finger knuckle print, which uses texture, geometric and shape-oriented features. Kumar and Zhihuan [18] explored the possibility of using second minor finger knuckle images for the personal recognition.…”

Section: Introductionmentioning

confidence: 99%

Feature-level fusion of major and minor dorsal finger knuckle patterns for person authentication

Attia

Akhtar

Chahir

2020

SIViP

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The identification of individuals by their finger dorsal patterns has become a very active area of research in recent years. In this paper, we present a multimodal biometric personal identification system that combines the information extracted from the finger dorsal surface image with the major and minor knuckle pattern regions. In particular, first the features are extracted from each single region by BSIF (binarized statistical image features) technique. Then, extracted information is fused at feature level. Fusion is followed by dimensionality reduction step using PCA (principal component analysis) + LDA (linear discriminant analysis) scheme in order to improve its discriminatory power. Finally, in the matching stage, the cosine Mahalanobis distance has been employed. Experiments were conducted on publicly available database for minor and major finger knuckle images, which was collected from 503 different subjects. Reported experimental results show that feature-level fusion leads to improved performance over single modality approaches, as well as over previously proposed methods in the literature.

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Personal recognition using finger knuckle shape oriented features and texture analysis

Cited by 19 publications

References 29 publications

HE-Co-HOG and k-SVM classifier for finger knuckle and palm print-based multimodal biometric recognition

HE-Co-HOG and k-SVM classifier for finger knuckle and palm print-based multimodal biometric recognition

Victory sign biometrie for terrorists identification: Preliminary results

Feature-level fusion of major and minor dorsal finger knuckle patterns for person authentication

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