Iris recognition through machine learning techniques: A survey

Marsico, Maria De; Petrosino, Alfredo; Ricciardi, Stefano

doi:10.1016/j.patrec.2016.02.001

Cited by 92 publications

(31 citation statements)

References 12 publications

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“…Traditionally, these systems recognize an individual using either a token-based method (such as a key or passwords) or biometric methods (that use the individual’s physical characteristics such as the face [7,8], finger-vein [9], fingerprint [10], or iris patterns [11,12] for recognition). Even though biometric features have proven to be more sufficient in recognizing persons in security systems because of biometric patterns’ advantages of being hard to steal and hard to fake [13], these kinds of biometric features require the cooperation of users and a short capturing distance (z-distance) between camera and user during the image acquisition stage.…”

Section: Introductionmentioning

confidence: 99%

Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras

Nguyen

Hong

Kim

et al. 2017

Sensors

489

159

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The human body contains identity information that can be used for the person recognition (verification/recognition) problem. In this paper, we propose a person recognition method using the information extracted from body images. Our research is novel in the following three ways compared to previous studies. First, we use the images of human body for recognizing individuals. To overcome the limitations of previous studies on body-based person recognition that use only visible light images for recognition, we use human body images captured by two different kinds of camera, including a visible light camera and a thermal camera. The use of two different kinds of body image helps us to reduce the effects of noise, background, and variation in the appearance of a human body. Second, we apply a state-of-the art method, called convolutional neural network (CNN) among various available methods, for image features extraction in order to overcome the limitations of traditional hand-designed image feature extraction methods. Finally, with the extracted image features from body images, the recognition task is performed by measuring the distance between the input and enrolled samples. The experimental results show that the proposed method is efficient for enhancing recognition accuracy compared to systems that use only visible light or thermal images of the human body.

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Section: Introductionmentioning

confidence: 99%

Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras

Nguyen

Hong

Kim

et al. 2017

Sensors

489

159

View full text Add to dashboard Cite

show abstract

“…Machine learning-based methods 243 Long-range iris recognition 244 Fingerprint recognition Fingerprint recognition for young children 245 Fingerprint recognition at crime scenes 246 Review works 247,248 Others Age and gender recognition 249,250 Facial expression recognition À À À CNN based methods 252,253 Multi-modality feature fusion-based method 254 Expression recognition based on static images 255 Micro-Expression Recognition [256][257][258] Facial expressions generation À À À Interactive GAN-based method 260 3D facial expression generation 261 Humanoid robot expression generation 23 Three-dimensional speaking characters 262 Expression generation natural description 264,265 Posture or gestures recognition À À À Driving posture recognition 266 Weighted fusion method for gesture recognition 267 Posture recognition for hazard prevention 268 Emotional body gesture recognition 269 Gesture recognition in video 271 Hand gesture recognition 272 deep neural network methods are introduced into the¯eld to seek a better recognition performance. The¯rst step of face recognition is the detection of face with an aim to determine whether faces exist on a given image or not.…”

Section: Iris Recognitionmentioning

confidence: 99%

A Review on Human-Computer Interaction and Intelligent Robots

Ren

Bao

2020

Int. J. Info. Tech. Dec. Mak.

100

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In the field of artificial intelligence, human–computer interaction (HCI) technology and its related intelligent robot technologies are essential and interesting contents of research. From the perspective of software algorithm and hardware system, these above-mentioned technologies study and try to build a natural HCI environment. The purpose of this research is to provide an overview of HCI and intelligent robots. This research highlights the existing technologies of listening, speaking, reading, writing, and other senses, which are widely used in human interaction. Based on these same technologies, this research introduces some intelligent robot systems and platforms. This paper also forecasts some vital challenges of researching HCI and intelligent robots. The authors hope that this work will help researchers in the field to acquire the necessary information and technologies to further conduct more advanced research.

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“…Deep learning is one of the most recent and promising machine learning techniques [28]. Thus, it is natural that there are still a few works that use and apply this technique in iris images.…”

Section: B Deep Learning In Iris Recognitionmentioning

confidence: 99%

The Impact of Preprocessing on Deep Representations for Iris Recognition on Unconstrained Environments

Zanlorensi

Luz

Laroca

et al. 2018

2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)

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The use of iris as a biometric trait is widely used because of its high level of distinction and uniqueness. Nowadays, one of the major research challenges relies on the recognition of iris images obtained in visible spectrum under unconstrained environments. In this scenario, the acquired iris are affected by capture distance, rotation, blur, motion blur, low contrast and specular reflection, creating noises that disturb the iris recognition systems. Besides delineating the iris region, usually preprocessing techniques such as normalization and segmentation of noisy iris images are employed to minimize these problems. But these techniques inevitably run into some errors. In this context, we propose the use of deep representations, more specifically, architectures based on VGG and ResNet-50 networks, for dealing with the images using (and not) iris segmentation and normalization. We use transfer learning from the face domain and also propose a specific data augmentation technique for iris images. Our results show that the approach using non-normalized and only circle-delimited iris images reaches a new state of the art in the official protocol of the NICE.II competition, a subset of the UBIRIS database, one of the most challenging databases on unconstrained environments, reporting an average Equal Error Rate (EER) of 13.98% which represents an absolute reduction of about 5%.

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Iris recognition through machine learning techniques: A survey

Cited by 92 publications

References 12 publications

Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras

Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras

A Review on Human-Computer Interaction and Intelligent Robots

The Impact of Preprocessing on Deep Representations for Iris Recognition on Unconstrained Environments

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