2D-human face recognition using SIFT and SURF descriptors of face’s feature regions

Gupta, Surbhi; Thakur, Kutub; Kumar, Munish

doi:10.1007/s00371-020-01814-8

Cited by 121 publications

(24 citation statements)

References 31 publications

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“…Table 2 shows the percentage recognition rate of individual ARLBP and FDCT technique and hybrid technique [FDCT +ARLBP] algorithm compared with the existing algorithm [27] for FERET database with image size of 130x150 with different descriptors and distance measures. Further, the proposed work is also compared with the existing algorithms for the NIR database [30] and FERET database [28,29,31,32,33]…”

Section: Number Of Genuine Samples Recognition Ratementioning

confidence: 99%

Performance Evaluation of Face Recognition system by Concatenation of Spatial and Transformation Domain Features

K¹,

Ravi²

2021

IJCNIS

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Face biometric system is one of the successful applications of image processing. Person recognition using face is the challenging task since it involves identifying the 3D object from 2D object. The feature extraction plays a very important role in face recognition. Extraction of features both in spatial as well as frequency domain has more advantages than the features obtained from single domain alone. The proposed work achieves spatial domain feature extraction using Asymmetric Region Local Binary Pattern (ARLBP) and frequency domain feature extraction using Fast Discrete Curvelet Transform (FDCT). The obtained features are fused by concatenation and compared with trained set of features using different distance metrics and Support Vector Machine (SVM) classifier. The experiment is conducted for different face databases. It is shown that the proposed work yields 95.48% accuracy for FERET, 92.18% for L-space k, 76.55% for JAFFE and 81.44% for NIR database using SVM classifier. The results show that the proposed system provides better recognition rate for SVM classifier when compare to the other distance matrices. Further, the work is also compared with existing work for performance evaluation.

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Section: Number Of Genuine Samples Recognition Ratementioning

confidence: 99%

Performance Evaluation of Face Recognition system by Concatenation of Spatial and Transformation Domain Features

K¹,

Ravi²

2021

IJCNIS

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“…digital images hold thousands of secret and important data, be it personal or medical images. Accordingly, many works have been carried out to process the images and discover the information they contain, whether medical images [5][6][7][8][9][10][11], face recognition [12,13], or vehicles [14,15]. Therefore, these images need a way to protect them from the information they contain.…”

Section: Objectives and Motivation Of The Workmentioning

confidence: 99%

Maneuvering Digital Watermarking In Face Recognition

Shahin¹,

Azim²,

Taloba³

2021

IJETT

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The challenges faced in the digital world are many, which could be resolved with some biometric recognition methods. These biometric recognition methods are encompassed within watermarking technology, steganography, cryptography, and many other schemes of security. These methods assist in securing digital images with the authentication of their owner. This paper briefly contextualizes the digital watermarking technique, which is referred to as Natural Preserving Transform (NPT) and Hartley Transform, which is endeavored in the face recognition process. The non-blind extraction and quasiblind extraction techniques are used for extracting the watermark from the image in the proposed system. This paper articulates the application of this watermarking technique employed in face recognition through watermarking of various face images to multiple backgrounds encompassing gray-scale images. Natural Preserve Transform is employed as a part of the fuzzy logic watermarking. In the proposed system, NPT is employed for encoding a logo of grayscale watermarking text or logo image to a host image located anywhere. The robustness and performance of the proposed system are experimentally tested with the help of image processing operations like image compression, noise degradation, cropping. Due to its unique feature of uniform distribution of face images, this technique is selected among other methods in digital watermarking. The system is tested for its efficiency with experimental analyses, which could be confirmed with the results of the simulation. The above system is proposed for copyright protection, authentication, and security requirements.

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“…Random Forest, K-Nearest Neighbours) have been also widely used in computer vision and modelling systems, i.e. Garg et al (2018), Kumar et al (2018), Gupta et al (2019a), Chhabra et al (2020), Bansal et al (2021a), Kumar et al (2021), Gupta et al (2021) and Bansal et al (2021b). In terms of resource planning in healthcare, a wide range of tools and techniques have been proposed including agent-based simulation (Cabrera et al 2012), discrete event simulation (Izady and Worthington 2012;Rossetti et al 1999;Ahmed and Alkhamis 2009), queuing theory (Belciug and Gorunescu 2015;Hou et al 2019), operating room scheduling (Adan et al 2009;Akbarzadeh et al 2019;Vandenberghe et al 2019) and ambulance deployment (Bertsimas and Ng 2019;Talarico et al 2015;Majzoubi et al 2012) among others.…”

Section: Introduction and Related Workmentioning

confidence: 99%

A hybrid analytical model for an entire hospital resource optimisation

2021

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Given the escalating healthcare costs around the world (more than 10% of the world's GDP) and increasing demand hospitals are under constant scrutiny in terms of managing services with limited resources and tighter budgets. Hospitals endeavour to find sustainable solutions for a variety of challenges ranging from productivity enhancements to resource allocation. For instance, in the UK, evidence suggests that hospitals are struggling due to increased delayed transfers of care, bed-occupancy rates well above the recommended levels of 85% and unmet A&E performance targets. In this paper, we present a hybrid forecasting-simulation-optimisation model for an NHS Foundation Trust in the UK. Using the Hospital Episode Statistics dataset for A&E, outpatient and inpatient services, we estimate the future patient demands for each speciality and model how it behaves with the forecasted activity in the future. Discrete event simulation is used to capture the entire hospital within a simulation environment, where the outputs is used as inputs into a multi-period integer linear programming (MILP) model to predict three vital resource requirements (on a monthly basis over a 1-year period), namely beds, physicians and nurses. We further carry out a sensitivity analysis to establish the robustness of solutions to changes in parameters, such as nurse-to-bed ratio. This type of modelling framework is developed for the first time to better plan the needs of hospitals now and into the future.

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2D-human face recognition using SIFT and SURF descriptors of face’s feature regions

Cited by 121 publications

References 31 publications

Performance Evaluation of Face Recognition system by Concatenation of Spatial and Transformation Domain Features

Performance Evaluation of Face Recognition system by Concatenation of Spatial and Transformation Domain Features

Maneuvering Digital Watermarking In Face Recognition

A hybrid analytical model for an entire hospital resource optimisation

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