Abstract
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.
Highlights
Results and Discussion e results of matching the two set test images for the Massachusetts Institute of Technology (MIT) and Japanese Female Facial Expressions (JAFFE) databases are shown in Figures 6 and 7, respectively
Numerical evaluation of the study algorithm gave a perfect (100%) average recognition rate each for recognition of occluded and augmented face images. is rate is slightly above the rates of Ayiah-Mensah et al [29] who used FFT-Principal Component Analysis (PCA)/Singular Value Decomposition (SVD) recognition algorithm and obtained 90% average recognition rate each on the same databases. is shows that the adopted preprocessing mechanism has an edge over the Fast Fourier transformation (FFT) mechanism used by Ayiah-Mensah et al [29]. e perfect (100%) rate of recognition achieved cannot be guaranteed if the level of missingness in the face images increases
According to Ayiah-Mensah et al [29], the failure of the numerical evaluation exercise to uncover this finding can be attributed to the fact that the statistical evaluation mechanism is a more data-driven approach to assess the performance of the recognition algorithm
Summary
Us, the use of image enhancement techniques and their effects on the performance of face recognition algorithms have been studied by several researchers [1]. Abdul-Jabbar [3] showed that preprocessing steps such as image adjustment, histogram equalization, and change in file format when applied to enhance the contrast and the quality of face images in different face recognition algorithms improve the accuracy of recognition up to 30% as compared to using the original database of face images. In the case where half of the face is degraded due to occlusions, several researchers [4, 5] have leveraged the bilateral symmetry of face images to reconstruct the full-face images and have used different denoising techniques to enhance image quality. The scope of their work was limited to only image degradation due to half-face occlusions. e problem of image degradation due to random missing pixels or patches was not addressed
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