Non-Orthogonal View Iris Recognition System

Chou, Chia-Te; Shih, Sheng-Wen; Chen, Wen-Shiung; Cheng, Victor W.; Chen, Duan-Yu

doi:10.1109/tcsvt.2009.2035849

Cited by 55 publications

(4 citation statements)

References 44 publications

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“…Chou et al [17] proposed a Non-orthogonal Iris recognition system based on four spectral (red, green, blue and Near-InfraRed) images. They proposed a circle rectification method to reduce the off-axis iris distortion.…”

Section: Related Work a Iris Recognition Hardwarementioning

confidence: 99%

Toward an Efficient Iris Recognition System on Embedded Devices

Benalcazar,

Tapia,

Vasquez

et al. 2023

IEEE Access

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Iris Recognition (IR) is one of the market's most reliable and accurate biometric systems. Today, it is challenging to build NIR-capturing devices under the premise of hardware price reduction. Commercial NIR sensors are protected from modification. The process of building a new device is not trivial because it is required to start from scratch with the process of capturing images with quality, calibrating operational distances, and building lightweight software such as eyes/iris detectors and segmentation subsystems. In light of such challenges, this work aims to develop and implement iris recognition software in an embedding system and calibrate NIR in a contactless binocular setup. We evaluate and contrast speed versus performance obtained with two embedded computers and infrared cameras. Further, a lightweight segmenter sub-system called "Unet_xxs" is proposed, which can be used for iris semantic segmentation under restricted memory resources. The evaluations reveal that Unet_xxs reduces the number of parameters by 77% and duplicates the speed of state-of-the-art segmentation models with an EER drop smaller than 1% in Iris Recognition with 8.06 fps and IOU of 0.8382. INDEX TERMSEmbedding systems, Iris sensor, NIR Camera, Hardware.

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Section: Related Work a Iris Recognition Hardwarementioning

confidence: 99%

Toward an Efficient Iris Recognition System on Embedded Devices

Benalcazar,

Tapia,

Vasquez

et al. 2023

IEEE Access

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“…The PA can be constructed with any type of biometric feature such as fingerprints [5], [6], [7], [8], [9], the iris of the eye [10], [11], [12] and the face [13], [14], [15].…”

Section: Previous Workmentioning

confidence: 99%

Creating Realistic Presentation Attacks for Facial Impersonation Step-by-Step

Gallardo-Cava,

Ortega-Delcampo,

Guillen-Garcia

et al. 2023

IEEE Access

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Presentation attacks are one of the many dangers facing law enforcement today. In addition, material science is constantly advancing and criminals, aware of this fact, are taking advantage of new composites to manufacture new artifacts that allow them to cross borders by breaching border control points. This article presents the creation of several presentation attacks using make-up, hyper-realistic latex, and prosthetic masks. It is worth noting that such attacks do not receive adequate attention, due to the difficulty in their elaboration. The work of professionals in the make-up sector is required. Each stage of processing is analyzed for any artifacts that would facilitate the detection of the attack, using a multispectral approach in the visible and thermal spectra. The methodology evaluates three different face recognition systems (FRS), the different stages of impersonation, i.e. when a specific part of the face such as nose, cheekbones, jaw, or eyes is incorporated. The results show that certain parts of the face improve impersonation and make it more difficult for the algorithms to detect possible impersonation. However, other parts of the face, such as the jaw, not only do not improve impersonation but also significantly worsen performance. Using OpenFace as an FRS example, which is one of the FRS employed in this research work, the bonafide comparison of the target yields a score of 0.304, while with the make-up attack before applying make-up to the jaw, it gives 0.291, and after applying make-up, it gives 0.421.

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“…For iris feature extraction there are reported methods that perform signal processing in both: (1) spatial domain, using mathematical operators such as Laplacian of Gaussian filters [2] and (2) frequency domain, using transformed as Gabor [3,4] and Wavelets [5,6]. Another group uses statistical processing techniques such as LBP [7], Co-Occurrence [8].…”

Section: Introductionmentioning

confidence: 99%

Iris Texture Description Using Ordinal Co-occurrence Matrix Features

Chacon-Cabrera

Zhang

Garea-Llano

et al. 2015

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Feature extraction is one of the fundamental steps of any biometric recognition system. The biometric iris recognition is not an exception. In the last 30 years a lot of algorithms have been proposed seeking a better description of the texture image of the human iris. The problem still remains into find features that are robust to the different conditions in which the iris images are captured. This paper proposes a new iris texture description based on ordinal co-occurrence matrix features for iris recognition scheme that increases the recognition accuracy. The novelty of this work is the new strategy in applying robust feature extraction method for texture description in iris recognition. The experiments with the Casia-Interval, Casia-Thousands and Ubiris-v1 databases show that our scheme increases the recognition accuracy and it is robust to different condition of image capture.

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Non-Orthogonal View Iris Recognition System

Cited by 55 publications

References 44 publications

Toward an Efficient Iris Recognition System on Embedded Devices

Toward an Efficient Iris Recognition System on Embedded Devices

Creating Realistic Presentation Attacks for Facial Impersonation Step-by-Step

Iris Texture Description Using Ordinal Co-occurrence Matrix Features

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