Recognition of Face Images Under Angular Constraints Using DWT-Pca/SVD Algorithm

Asiedu, Louis; Mettle, Felix O.; Nortey, Ezekiel N. N.; Yeboah, Enoch S.

doi:10.17654/ms102112809

Cited by 4 publications

(10 citation statements)

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“…Define L as low frequency band and H as high frequency band. In DWT, the LL subband represents the lower resolution estimate of the original value, while mid-frequency and high-frequency details subband HL, LH, and HH represent horizontal edge, vertical edge, and diagonal edge details, respectively [7]. Most of the energy is concentrated in low frequency subband, and that is why the LL subband (the approximate coefficients of the decomposition) is the only subband among the four subbands used to produce the next level of decomposition.…”

Section: Discrete Wavelet Transform (Dwt)mentioning

confidence: 99%

“…The facial expression features are captured in the HL subband, whereas the face pose features are captured in LH subband (the vertical features of outline). The subband HH is the unstable band in all subbands because it is easily disturbed by noises, expressions, and poses, whereas the subband LL is the most stable subband [7].…”

Section: Discrete Wavelet Transform (Dwt)mentioning

confidence: 99%

“…It is also worthy to note that in the transformation process described above, we rely on the proposed convolution theorem by Wei and Li [10] which states that "a modified ordinary convolution in time domain is equivalent to simple multipli-cation operations for Offset Linear Canonical transform (OLCT) and Fourier transform." According to Asiedu et al [7], if we consider a vectorized image X j of dimension N where N is even, then the single-level Haar transform decomposes X j into two signals of length N/2. These are the mean coefficient vector U 1 with components…”

Section: Discrete Wavelet Transform (Dwt)mentioning

confidence: 99%

“…The performance of the DWT-PCA/SVD face recognition algorithm on varying head tilt/pose was evaluated by Asiedu et al [7]. Their study revealed that the recognition rate of the DWT-PCA/SVD algorithm declines for headposes greater than 20°.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evaluation of the DWT-PCA/SVD Recognition Algorithm on Reconstructed Frontal Face Images

Asiedu

Essah

Iddi

et al. 2021

Journal of Applied Mathematics

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The face is the second most important biometric part of the human body, next to the finger print. Recognition of face image with partial occlusion (half image) is an intractable exercise as occlusions affect the performance of the recognition module. To this end, occluded images are sometimes reconstructed or completed with some imputation mechanism before recognition. This study assessed the performance of the principal component analysis and singular value decomposition algorithm using discrete wavelet transform (DWT-PCA/SVD) as preprocessing mechanism on the reconstructed face image database. The reconstruction of the half face images was done leveraging on the property of bilateral symmetry of frontal faces. Numerical assessment of the performance of the adopted recognition algorithm gave average recognition rates of 95% and 75% when left and right reconstructed face images were used for recognition, respectively. It was evident from the statistical assessment that the DWT-PCA/SVD algorithm gives relatively lower average recognition distance for the left reconstructed face images. DWT-PCA/SVD is therefore recommended as a suitable algorithm for recognizing face images under partial occlusion (half face images). The algorithm performs relatively better on left reconstructed face images.

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Section: Discrete Wavelet Transform (Dwt)mentioning

confidence: 99%

Section: Discrete Wavelet Transform (Dwt)mentioning

confidence: 99%

Section: Discrete Wavelet Transform (Dwt)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of the DWT-PCA/SVD Recognition Algorithm on Reconstructed Frontal Face Images

Asiedu

Essah

Iddi

et al. 2021

Journal of Applied Mathematics

Self Cite

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show abstract

“…L is denoted as the low-frequency band and H as the high-frequency band. Specifically the LL subband represents the lower resolution estimate of the original value, while midfrequency and high-frequency detail subbands HL, LH, and HH represent horizontal edge, vertical edge, and diagonal edge details, respectively [22]. e LL subband can further be decomposed to obtain another level of decomposition.…”

Section: Discrete Wavelet Transform (Dwt)mentioning

confidence: 99%

Assessing the Effect of Data Augmentation on Occluded Frontal Faces Using DWT-PCA/SVD Recognition Algorithm

Asiedu

Mensah

Ayiah-Mensah

et al. 2021

Advances in Multimedia

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The drift towards face-based recognition systems can be attributed to recent advances in supportive technology and emerging areas of application including voting systems, access control, human-computer interactions, entertainments, and crime control. Despite the obvious advantages of such systems being less intrusive and requiring minimal cooperation of subjects, the performances of their underlying recognition algorithms are challenged by the quality of face images, usually acquired from uncontrolled environments with poor illuminations, varying head poses, ageing, facial expressions, and occlusions. Although several researchers have leveraged on the property of bilateral symmetry to reconstruct half-occluded face images, their approach becomes deficient in the presence of random occlusions. In this paper, we harnessed the benefits of the multiple imputation by the chained equation technique and image denoising using Discrete Wavelet Transforms (DWTs) to reconstruct degraded face images with random missing pixels. Numerical evaluation of the study algorithm gave a perfect (100%) average recognition rate each for recognition of occluded and augmented face images. The study also revealed that the average recognition rate for the augmented face images (75.5811) was significantly lower than the average recognition rate (430.7153) of the occluded face images. MICE augmentation is recommended as a suitable data enhancement mechanism for imputing missing data/pixel of occluded face images.

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On the search for efficient face recognition algorithm subject to multiple environmental constraints

Essel,

Mensah,

Ocran

et al. 2024

Heliyon

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Recognition of Face Images Under Angular Constraints Using DWT-Pca/SVD Algorithm

Cited by 4 publications

References 0 publications

Evaluation of the DWT-PCA/SVD Recognition Algorithm on Reconstructed Frontal Face Images

Evaluation of the DWT-PCA/SVD Recognition Algorithm on Reconstructed Frontal Face Images

Assessing the Effect of Data Augmentation on Occluded Frontal Faces Using DWT-PCA/SVD Recognition Algorithm

On the search for efficient face recognition algorithm subject to multiple environmental constraints

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