Recognition of Augmented Frontal Face Images Using FFT-PCA/SVD Algorithm
In spite of the differences in visual stimulus of human beings such as ageing, changing conditions of a person, and occlusion, recognition can even be done at a glance by the human eye many years after the previous encounter. It has been established that facial differences like the hairstyle changes...
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| Format: | Article |
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Wiley
2021-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2021/6686759 |
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| author | Francis Ayiah-Mensah Louis Asiedu Felix O. Mettle Richard Minkah |
| author_facet | Francis Ayiah-Mensah Louis Asiedu Felix O. Mettle Richard Minkah |
| author_sort | Francis Ayiah-Mensah |
| collection | DOAJ |
| description | In spite of the differences in visual stimulus of human beings such as ageing, changing conditions of a person, and occlusion, recognition can even be done at a glance by the human eye many years after the previous encounter. It has been established that facial differences like the hairstyle changes, growing of one’s beard, wearing of glasses, and other forms of occlusions can hardly hinder the power of the human brain from making a face recognition. However, the same cannot easily be said about automated intelligent systems which have been developed to mimic the skill of the human brain to aid in recognition. There have been growing interests in developing a resilient and efficient recognition system mainly because of its numerous application areas (access control, entertainment/leisure, security system based on biometric data, and user-friendly human-machine interfaces). Although there have been numerous researches on face recognition under varying pose, illumination, expression, and image degradations, problems caused by occlusions are mostly ignored. This study thus focuses on facial occlusions and proposes an enhancement mechanism through face image augmentation to improve the recognition of occluded face images. This study assessed the performance of Principal Component Analysis with Singular Value Decomposition using Fast Fourier Transform (FFT-PCA/SVD) for preprocessing face recognition algorithm on face images with missingness and augmented face image database. It was found that the average recognition rates for the FFT-PCA/SVD algorithm were the same (90%) when face images with missingness and augmented face images were used as test images, respectively. The statistical evaluation revealed that there exists a significant difference in the average recognition distances for the face images with missingness and augmented face images when FFT-PCA/SVD is used for recognition. Augmented face images tend to have a relatively lower average recognition distance when used as test images. This finding is contrary to the equal performance assessment by the adopted numerical technique. The MICE algorithm is therefore recommended as a suitable imputation mechanism for enhancing/improving the performance of the face recognition system. |
| format | Article |
| id | doaj-art-b8a70b7394904af5b6bf35aa65c4e3d5 |
| institution | DOAJ |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-b8a70b7394904af5b6bf35aa65c4e3d52025-08-20T03:22:50ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322021-01-01202110.1155/2021/66867596686759Recognition of Augmented Frontal Face Images Using FFT-PCA/SVD AlgorithmFrancis Ayiah-Mensah0Louis Asiedu1Felix O. Mettle2Richard Minkah3Department of Statistics & Actuarial Science, School of Physical and Mathematical Sciences, University of Ghana, Legon, Accra, GhanaDepartment of Statistics & Actuarial Science, School of Physical and Mathematical Sciences, University of Ghana, Legon, Accra, GhanaDepartment of Statistics & Actuarial Science, School of Physical and Mathematical Sciences, University of Ghana, Legon, Accra, GhanaDepartment of Statistics & Actuarial Science, School of Physical and Mathematical Sciences, University of Ghana, Legon, Accra, GhanaIn spite of the differences in visual stimulus of human beings such as ageing, changing conditions of a person, and occlusion, recognition can even be done at a glance by the human eye many years after the previous encounter. It has been established that facial differences like the hairstyle changes, growing of one’s beard, wearing of glasses, and other forms of occlusions can hardly hinder the power of the human brain from making a face recognition. However, the same cannot easily be said about automated intelligent systems which have been developed to mimic the skill of the human brain to aid in recognition. There have been growing interests in developing a resilient and efficient recognition system mainly because of its numerous application areas (access control, entertainment/leisure, security system based on biometric data, and user-friendly human-machine interfaces). Although there have been numerous researches on face recognition under varying pose, illumination, expression, and image degradations, problems caused by occlusions are mostly ignored. This study thus focuses on facial occlusions and proposes an enhancement mechanism through face image augmentation to improve the recognition of occluded face images. This study assessed the performance of Principal Component Analysis with Singular Value Decomposition using Fast Fourier Transform (FFT-PCA/SVD) for preprocessing face recognition algorithm on face images with missingness and augmented face image database. It was found that the average recognition rates for the FFT-PCA/SVD algorithm were the same (90%) when face images with missingness and augmented face images were used as test images, respectively. The statistical evaluation revealed that there exists a significant difference in the average recognition distances for the face images with missingness and augmented face images when FFT-PCA/SVD is used for recognition. Augmented face images tend to have a relatively lower average recognition distance when used as test images. This finding is contrary to the equal performance assessment by the adopted numerical technique. The MICE algorithm is therefore recommended as a suitable imputation mechanism for enhancing/improving the performance of the face recognition system.http://dx.doi.org/10.1155/2021/6686759 |
| spellingShingle | Francis Ayiah-Mensah Louis Asiedu Felix O. Mettle Richard Minkah Recognition of Augmented Frontal Face Images Using FFT-PCA/SVD Algorithm Applied Computational Intelligence and Soft Computing |
| title | Recognition of Augmented Frontal Face Images Using FFT-PCA/SVD Algorithm |
| title_full | Recognition of Augmented Frontal Face Images Using FFT-PCA/SVD Algorithm |
| title_fullStr | Recognition of Augmented Frontal Face Images Using FFT-PCA/SVD Algorithm |
| title_full_unstemmed | Recognition of Augmented Frontal Face Images Using FFT-PCA/SVD Algorithm |
| title_short | Recognition of Augmented Frontal Face Images Using FFT-PCA/SVD Algorithm |
| title_sort | recognition of augmented frontal face images using fft pca svd algorithm |
| url | http://dx.doi.org/10.1155/2021/6686759 |
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