A Novel Unsharp Mask Sharpening Method in Preprocessing for Face Recognition

Xie, Liping; Guo, Jingjing; Li, Zhihui

doi:10.1109/imccc.2015.87

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…Hao et al [76] presented an SR face reconstruction technique based on Nonlocal Similarity and Multi-Scale Linear Combination Consistency (NLS-MLC) and Resolution Scale Invariant Feature (RSIF). Lanchi et al [77] proposed an image enhancement method based on Unsharp mask for the preprocessing module in a face recognition system. Using images from the World Wide Web to evaluate their system, their results was compared with Polesel et al [78] results and Lanchi et al [77] claimed that their enhancement method is much more efficient in term of processing time with a run-time of 21miliseconds per frame compared to Polesel et al [78] run-time of 31miliseconds per frame.…”

Section: Low Image Resolutionmentioning

confidence: 99%

Face Recognition State-of-the-art, Enablers, Challenges and Solutions: A Review

Moung¹

2020

IJATCSE

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In the past decade, face recognition has gained an important role among the most frequently used image processing applications and the availability of viable technologies in this field has also contributed significantly to this. Face recognition has become an enabler in healthcare, surveillance, photo cataloging, attendance, and much more, which will be discussed in this review paper. Despite rapid progress in face-recognition technology, various challenges such as variations, occlusion, facial expressions, aging and many more that affect the performance of the system still need to be addressed. This paper presents a review on the state-of-the-art, enablers, challenges and solutions for face recognition. Face recognition can be categorized into three groups; namely global approach, local feature approach, and hybrid approach. The global approach uses the whole face as input for the face recognition system. The local approach uses measurements between important landmarks of a face and certain face region selection for training. The hybrid approach blends global and local approaches in which the hybrid approach uses the best global and local approach methods. The challenges of face recognition are; (i) automated face detection where difficulties lies on detecting a person's face, (ii) pose variations cause by rotation of people's head, (iii) face occlusion, (iv) facial expression changes, (v) ageing of the face, (vi) varying illumination conditions, (vii) low image resolution, (viii) identity look-alike or twin, and (ix) other technical difficulties. Finally, the solutions to each of the highlighted challenges were described. The survey found that all the images considered for training and testing were made up of RGB images. With the rapid growth of computer technology in terms of computing speed and the increasingly sophisticated functions of smartphones, multispectral or even hyperspectral imagery could be considered for face-recognition research.

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Section: Low Image Resolutionmentioning

confidence: 99%

Face Recognition State-of-the-art, Enablers, Challenges and Solutions: A Review

Moung¹

2020

IJATCSE

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An adaptive block based un-sharp masking for image quality enhancement

Mortezaie

Hassanpour

Amiri

2019

Multimed Tools Appl

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Recognition of asphalt road hazards based on high-density gray point clouds

et al. 2022

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Road hazards can lead to dangerous accidents and endanger the safety of pedestrians. Frequent and thorough road inspection is required to maintain road safety. This paper proposes an improved U-Net model that combines gray-scale images and depth images and uses a data statistics method based on a road depth map to eliminate hazard-free data automatically and reduce the computational complexity involved in hazard detection. Experiments showed that the proposed model, based on the improved U-Net-based pavement hazard recognition and extraction algorithm, could smoothly and efficiently extract pavement cracks and deformation hazards in complex scenes with noise interference and produce results with strong robustness. Comprehensive indicators such as global recognition accuracy rate A (Accuracy), precision rate P (Precision), recall rate R (Recall), evaluation index F1 (F -Measure), and Mean Intersection over Union (MIoU) were used to assess the effectiveness of the proposed model in comparison with existing hazard detection models; the proposed model greatly outperformed the models in all the indicators. The proposed model can provide a significant reference for subsequent pavement repair work and be used to improve road safety.

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A Novel Unsharp Mask Sharpening Method in Preprocessing for Face Recognition

Cited by 4 publications

References 8 publications

Face Recognition State-of-the-art, Enablers, Challenges and Solutions: A Review

Face Recognition State-of-the-art, Enablers, Challenges and Solutions: A Review

An adaptive block based un-sharp masking for image quality enhancement

Recognition of asphalt road hazards based on high-density gray point clouds

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