RETRACTED ARTICLE: An effective deep learning features based integrated framework for iris detection and recognition

Jayanthi, J.; Lydia, E. Laxmi; Krishnaraj, N.; Jayasankar, T.; Babu, R.; Suji, R. Adaline

doi:10.1007/s12652-020-02172-y

Cited by 50 publications

(25 citation statements)

References 17 publications

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“…The smart, app-based production management system has both hardware and software parts. When the power is on, pic value is read using manual input [14]. Each rotation of shaft represents one wrap of cloth the rotation is monitored using proximity sensor [15][16].…”

Section: Block Diagrammentioning

confidence: 99%

Application Based Production Management System In Textile Industries

al.¹

2021

TURCOMAT

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Presently, there is lack of proper management system to manage orders and analyze the daily reports which is prerequisite to acquire production management systems. This paper objectives to elucidate the production management system in small and medium sized Enterprises manufacturing apparent in India. SMES rely invariably on register and manual - data sheet based management which are detrimental in the management process. This proposed system provides a cost-effective solution to the above said problem through app-based platform, it reveals the production planning through reports of the data collected to analyze the daily production on various parameters involved in apparel production. This system provides a daily report to the authorized person (Owner/ Supervisor) to facilitate production management and solve issues pertaining to production output addressing various implements involved in the process.

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Section: Block Diagrammentioning

confidence: 99%

Application Based Production Management System In Textile Industries

al.¹

2021

TURCOMAT

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“…Jayanthi et al (2020) proposed an integrated model based on effective DL for accurate iris detection, segmentation, and recognition. The CNN was adopted for data acquisition, and the final monitoring accuracy reached 99.14% [13]. Agarwal et al (2020) proposed a novel and proficient feature descriptor for local binary image detection of pseudo iris detection, and the proposed model showed high performance in different data dimensions [14].…”

Section: Research On Iris Detection and Recognitionmentioning

confidence: 99%

Iris Image Acquisition and Real-Time Detection System Using Convolutional Neural Network

Hu¹,

Si²

2021

Preprint

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The aim is to further improve the efficiency of iris detection and ensure real-time iris data acquisition. Here, the light field refocusing algorithm can collect the data in real-time based on the existing iris data acquisition and detection system, and the DL (Deep Learning) CNN (Convolutional Neural Network) is introduced. Consequently, an iris image acquisition and real-time detection system based on CNN is proposed, and the system for image acquisition, processing, and displaying is constructed based on FPGA (Field Programmable Gate Array). The spatial filtering algorithm can compare the performance of the proposed bilateral filters with common filters. The results indicate that the proposed bilateral filters can pick out qualified iris images in real-time, greatly improving the accuracy of the iris image recognition system. The average time for real-time quality assessment of each frame image is less than 0.05 seconds. The classification accuracy of the iris image quality assessment algorithm based on DL is 96.38%, higher than the other two algorithms, and the average classification error rate is 3.69%, lower than the average error rate of other algorithms. The results can provide a reference for real-time iris image detection and data acquisition.

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“…Handcrafted feature extraction approaches have been outclassed by CNN, with its capability to automatically learn relevant features from sufficient training data [ 17 , 18 ]. Recent advances in iris recognition have studied the feasibility of applying the CNN to iris image processing, such as iris segmentation [ 19 , 20 ], iris recognition [ 21 , 22 , 23 ], and fake iris detection [ 24 , 25 ]. Previous studies on iris recognition [ 21 , 26 ] indicated that the CNN-based methods could effectively learn the inherent characteristics of iris images and achieve superior performance than the classic iris matching method represented by IrisCode [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Accurate Iris Recognition Algorithm Based on a Novel Condensed 2-ch Deep Convolutional Neural Network

Liu

Zhou

Lan

et al. 2021

Sensors

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Recently, deep learning approaches, especially convolutional neural networks (CNNs), have attracted extensive attention in iris recognition. Though CNN-based approaches realize automatic feature extraction and achieve outstanding performance, they usually require more training samples and higher computational complexity than the classic methods. This work focuses on training a novel condensed 2-channel (2-ch) CNN with few training samples for efficient and accurate iris identification and verification. A multi-branch CNN with three well-designed online augmentation schemes and radial attention layers is first proposed as a high-performance basic iris classifier. Then, both branch pruning and channel pruning are achieved by analyzing the weight distribution of the model. Finally, fast finetuning is optionally applied, which can significantly improve the performance of the pruned CNN while alleviating the computational burden. In addition, we further investigate the encoding ability of 2-ch CNN and propose an efficient iris recognition scheme suitable for large database application scenarios. Moreover, the gradient-based analysis results indicate that the proposed algorithm is robust to various image contaminations. We comprehensively evaluated our algorithm on three publicly available iris databases for which the results proved satisfactory for real-time iris recognition.

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RETRACTED ARTICLE: An effective deep learning features based integrated framework for iris detection and recognition

Cited by 50 publications

References 17 publications

Application Based Production Management System In Textile Industries

Application Based Production Management System In Textile Industries

Iris Image Acquisition and Real-Time Detection System Using Convolutional Neural Network

An Efficient and Accurate Iris Recognition Algorithm Based on a Novel Condensed 2-ch Deep Convolutional Neural Network

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