Finger contour profile based hand biometric recognition

Bera, Asish; Bhattacharjee, Debotosh; Nasipuri, Mita

doi:10.1007/s11042-016-4075-x

Cited by 14 publications

(13 citation statements)

References 35 publications

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“…As spacer pins can deform the silhouette of the hand [64], systems without spacer pins have come to the fore in scientific circles [53,65,66]. The latest possibilities are contactless systems [38,61,67,68], where, in addition to the spacer pins, direct contact with the reader is not required. These systems are considered to be the most user-friendly, mainly due to the elimination of the need for physical contact.…”

Section: Sensormentioning

confidence: 99%

“…They are also used palm-based textures [85]. The silhouette is also distinguishable by its contour pixels; it represents the real shape of the hand [67].…”

Section: Extraction Of Biometric Featuresmentioning

confidence: 99%

“…The issue of normalization of various hand positions and accurate localization of fingertips and finger valleys in unrestricted and contactless systems requires further research. [67] Some systems extract biometric features of hand geometry and hand shape at the same time, extractors are specially adapted to the shape and geometry of the hand [86,79].…”

Section: Extraction Of Biometric Featuresmentioning

confidence: 99%

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Hand-Based Biometric Recognition Technique – Survey

Prihodova¹,

Hub²

2020

Adv. sci. technol. eng. syst. j.

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In today's modern world, reliable automatic personal recognition is a crucial area of discussion, primarily because of the increased security risks. A large number of systems first require the recognition of a person before they can access their services. Biometric recognition can be used, which can be understood as automatic identification or automatic verification of persons based on physiological or behavioural characteristics. Examples of biometric characteristics may be the fingerprint, face, signature, hand geometry. There are many approaches, and each has its pros and cons. However, there is currently no current extensive research and evaluation of hand-based biometric systems. Given the userfriendliness of these systems and the needs of society, this article aims to compare different methods of biometric recognition based on hand, so that the article reduces the entry barrier into this area of research. Furthermore, the article aims to determine the research gap and suggest possible directions for research in the future.

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Section: Sensormentioning

confidence: 99%

“…They are also used palm-based textures [85]. The silhouette is also distinguishable by its contour pixels; it represents the real shape of the hand [67].…”

Section: Extraction Of Biometric Featuresmentioning

confidence: 99%

Section: Extraction Of Biometric Featuresmentioning

confidence: 99%

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Hand-Based Biometric Recognition Technique – Survey

Prihodova¹,

Hub²

2020

Adv. sci. technol. eng. syst. j.

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“…Among the different biometrics analysed, the biological characteristics of hand such as vein or line patterns have been playing a substantial role in instituting human identity in most of the financial as well as forensic related applications (Jaswal, Kaul, & Nath, 2016; Zhang, Zhang, Zhang, & Zhu, 2010). These genetic patterns can be considered as discriminative biometric traits because of their supposedly unique and stable anatomical structures over time (Bera, Bhattacharjee, & Nasipuri, 2016; Michael et al, 2008). Although fingerprint is highly discriminable and perhaps the most successful form of hand‐based authentication mechanism (Cappelli, Ferrara, & Maltoni, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Also, fingerprint scans need high resolution (>400 dpi) images for better recognition results (Cappelli et al, 2010; Jaswal, Nigam, & Nath, 2017b). Unlike a fingerprint, the other hand patterns can be used even with low‐resolution images to identify suspects that enhances the system's accuracy and speed (Bera et al, 2016; Jaswal et al, 2016). Palm print's prime advantages include its huge public acceptance and multitexture (region of interest) ROI as compared with fingerprint (Zhang et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Selection of optimized features for fusion of palm print and finger knuckle‐based person authentication

Jaswal

Poonia

2020

Expert Systems

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The impact of digital technology in biometrics is much more efficient at interpreting data than humans, which results in completely replacement of manual identification procedures in forensic science. Because the single modality-based biometric frameworks limit performance in terms of accuracy and anti-spoofing capabilities due to the presence of low quality data, therefore, information fusion of more than one biometric characteristic in pursuit of high recognition results can be beneficial. In this article, we present a multimodal biometric system based on information fusion of palm print and finger knuckle traits, which are least associated to any criminal investigation as evidence yet. The proposed multimodal biometric system might be useful to identify the suspects in case of physical beating or kidnapping and establish supportive scientific evidences, when no fingerprint or face information is present in photographs. The first step in our work is data preprocessing, in which region of interest of palm and finger knuckle images have been extracted. To minimize nonuniform illumination effects, we first normalize the detected circular palm or finger knuckle and then apply line ordinal pattern (LOP)-based encoding scheme for texture enrichment. The nondecimated quaternion wavelet provides denser feature representation at multiple scales and orientations when extracted over proposed LOP encoding and increases the discrimination power of line and ridge features. To best of our knowledge, this first attempt is a combination of backtracking search algorithm and 2D 2 LDA has been employed to select the dominant palm and knuckle features for classification. The classifiers output for two modalities are combined at unsupervised rank level fusion rule through Borda count method, which shows an increase in performance in terms of recognition and verification, that is, 100% (correct recognition rate), 0.26% (equal error rate), 3.52 (discriminative index), and 1,262 m (speed).

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