Discrete Curvelet Transform Algorithm for Image Compression System

Ja’afar, Noor Huda

doi:10.30534/ijatcse/2020/3091.12020

Cited by 4 publications

(1 citation statement)

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“…Curvelet transform at scale 3 and angle 8 or 16 is sufficient to deconstruct faces. Images can be decreased in size before being subjected to feature extraction if necessary [16,17].…”

Section: Figure 1 Different Between Wavelet and Curveletmentioning

confidence: 99%

Biometrics Face Recognition Using Method of Wavelet and Curvelet Transforms with COVID-19

Alaluosi¹,

Mohammed²

2022

RCES

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During last two decades the subject of face recognition has become a major issue. It has been used in several important real-world applications such as database security, video surveillance, smart card, internet and intranet. The people’s ability to recognize a face is reduced if the person has a mask covering the lower part of face, so it may be focus now on, the eyes and the individual features on upper part of face. To extract the features, the Wavelet and Curvelet transforms proved its efficiency due to its higher for detection of curves and lines, which recognize the human’s face, these features are used to identify the enrolled persons and it should be stored in the template system to be used later in the recognition system. The result of this paper is to recognize the face of person with mask. The system performance was evaluated depend on face database (kaggle) for face-recognition with face mask during COVID-19 period. The results indicate that the proposed system showed good results, and it outperforms the other algorithms that used in face-recognition.

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“…Curvelet transform at scale 3 and angle 8 or 16 is sufficient to deconstruct faces. Images can be decreased in size before being subjected to feature extraction if necessary [16,17].…”

Section: Figure 1 Different Between Wavelet and Curveletmentioning

confidence: 99%