Person identification using fusion of iris and periocular deep features

Umer, Saiyed; Sardar, Alamgir; Dhara, Bibhas Chandra; Rout, Ranjeet Kumar; Pandey, Hari Mohan

doi:10.1016/j.neunet.2019.11.009

Cited by 56 publications

(20 citation statements)

References 52 publications

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“…The performances of the Active Contour in relation to the integro-differential operator, and the Hough Transform are presented in Table 1 until Table 4. These tables appear to present the results using varying number of iris and iris per individual, and these could be related to the results reported by [27][28].…”

Section: Discussion Of Resultssupporting

confidence: 57%

Investigation of iris segmentation techniques using active contours for non-cooperative iris recognition

Akinfende

Imoize

Ajose

2020

IJEECS

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<span>Iris image segmentation process based on graphical user interface (GUI) to accurately localize the iris structure is presented in this paper. The major challenge confronting the precision of an iris recognition model is how to determine the accuracy of the iris segmentation and localization. There are varying parameters that introduce constraints during feature extraction and these greatly affect the matching performance during iris localization. To this end, the Integro-differential operator, which involves the detection of inner and outer regions of the iris, and the circular hough transform, which is capable of detecting the circular boundary from the edge mapping were investigated, and an active contour model was evolved. In the evolved model, an emerging curve mapped with the zeros of the data set function is experimentally exploited. To demonstrate the suitability of the model for precise iris recognition, its parameters were compared against other related models. Simulation results show that the model has higher flexibility of substitution of images, and the images could be analyzed more accurately with less false rejections (FR) and false acceptance (FA) in comparison with the integro-differential operator. This implies that images could be analyzed faster using the evolved model, and easily substituted especially in situations where the need to care for numerous eye patients occur.</span>

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Section: Discussion Of Resultssupporting

confidence: 57%

Investigation of iris segmentation techniques using active contours for non-cooperative iris recognition

Akinfende

Imoize

Ajose

2020

IJEECS

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“…21 The fusing information obtained from the iris and pupil or periocular systems increases the performance of personal identification systems. 21,22 Personal identification systems based on ocular information have already been put to practical use.…”

Section: Ocular Informationmentioning

confidence: 99%

Personal identification systems for persons with dementia: a systematic review

Matsuda

Yoshimura

2021

Psychogeriatrics

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People with dementia can become missing and unable to be identified due to their cognitive decline and may not have belongings, including personally identifiable information. The aim of this systematic review was to summarise and discuss the current status of personal identification systems for missing and unidentified persons with dementia. An electronic systematic literature search was conducted by using PubMed, Web of Science, Cochrane Library, and CINAHL. By performing an electronic literature search, 128 texts were extracted. However, only three eligible texts were included in this review, and these were divided on the basis of the analysis target as follows: medical and human community support systems and digital systems. It is important to develop medical and social care systems and to construct and advance social personal identification systems, including medical and human community support systems and digital systems, for persons with dementia. However, this systematic review clarified that there have only been a few reports about the development and construction of social systems for missing and unidentified persons with dementia. People who interact with persons with dementia should consider this problem, which is increasing each year, and strive to build a social system that can be applied to personal identification as soon as possible.

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“…For generating the specific and concise representation of each modality, they applied a maxout in the CNN and then merged the distinct features of both modalities with the help of a weighted concatenation process. In the same manner, Umer et al [28] combined the periocular and iris features for the biometric recognition of a person. They deployed different deep learning based CNN frameworks such as ResNet-50, VGG-16 and Inception-v3 for feature extraction and classification.…”

Section: Related Workmentioning

confidence: 99%

Deep Feature Fusion of Fingerprint and Online Signature for Multimodal Biometrics

et al. 2021

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The extensive research in the field of multimodal biometrics by the research community and the advent of modern technology has compelled the use of multimodal biometrics in real life applications. Biometric systems that are based on a single modality have many constraints like noise, less universality, intra class variations and spoof attacks. On the other hand, multimodal biometric systems are gaining greater attention because of their high accuracy, increased reliability and enhanced security. This research paper proposes and develops a Convolutional Neural Network (CNN) based model for the feature level fusion of fingerprint and online signature. Two types of feature level fusion schemes for the fingerprint and online signature have been implemented in this paper. The first scheme named early fusion combines the features of fingerprints and online signatures before the fully connected layers, while the second fusion scheme named late fusion combines the features after fully connected layers. To train and test the proposed model, a new multimodal dataset consisting of 1400 samples of fingerprints and 1400 samples of online signatures from 280 subjects was collected. To train the proposed model more effectively, the size of the training data was further increased using augmentation techniques. The experimental results show an accuracy of 99.10% achieved with early feature fusion scheme, while 98.35% was achieved with late feature fusion scheme.

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Person identification using fusion of iris and periocular deep features

Cited by 56 publications

References 52 publications

Investigation of iris segmentation techniques using active contours for non-cooperative iris recognition

Investigation of iris segmentation techniques using active contours for non-cooperative iris recognition

Personal identification systems for persons with dementia: a systematic review

Deep Feature Fusion of Fingerprint and Online Signature for Multimodal Biometrics

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