Improving Face Recognition Rate with Image Preprocessing

Dharavath, Krishna; Talukdar, Fazal A.; Laskar, Rabul Hussain

doi:10.17485/ijst/2014/v7i8.26

Cited by 33 publications

(14 citation statements)

References 21 publications

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“…Preprocessing. According to Dharavath et al [1], image processing techniques enhance the quality of a captured image and improve the recognition rate. They further stated that, irrespective of the resolution and illumination of an image and the image preprocessing technique used, image preprocessing has an unequivocally significant impact on face recognition.…”

Section: Methodsmentioning

confidence: 99%

“…According to Dharavath et al [1], face recognition has stood out in recent times as a unique method of biometric recognition and is greatly sought after and implemented by industries and governments. This is largely because face recognition systems require minimal or no cooperation of subjects as compared to other identification methods based on biometrics such as fingerprint, iris, voice, and hand geometry.…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Performance of DWT-PCA/SVD Face Recognition Algorithm under Multiple Constraints

Mensah

Asiedu

Mettle

et al. 2021

Journal of Applied Mathematics

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Many architectures of face recognition modules have been developed to tackle the challenges posed by varying environmental constraints such as illumination, occlusions, pose, and expressions. These recognition systems have mainly focused on a single constraint at a time and have achieved remarkable successes. However, the presence of multiple constraints may deteriorate the performance of these face recognition systems. In this study, we assessed the performance of Principal Component Analysis and Singular Value Decomposition using Discrete Wavelet Transform (DWT-PCA/SVD) for preprocessing face recognition algorithm on multiple constraints (partially occluded face images acquired with varying expressions). Numerical evaluation of the study algorithm gave reasonably average recognition rates of 77.31% and 76.85% for left and right reconstructed face images with varying expressions, respectively. A statistically significant difference was established between the average recognition distance of the left and right reconstructed face images acquired with varying expressions using pairwise comparison test. The post hoc analysis using the Bonferroni simultaneous confidence interval revealed that the significant difference established through the pairwise comparison test was mainly due to the sad expressions. Although the performance of the DWT-PCA/SVD algorithm declined as compared to its performance on single constraints, the algorithm attained appreciable performance level under multiple constraints. The DWT-PCA/SVD recognition algorithm performs reasonably well for recognition when partial occlusion with varying expressions is the underlying constraint.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing the Performance of DWT-PCA/SVD Face Recognition Algorithm under Multiple Constraints

Mensah

Asiedu

Mettle

et al. 2021

Journal of Applied Mathematics

View full text Add to dashboard Cite

show abstract

“…Many neural network architectures have demonstrated the capability to identify objects from within specific sample images [ 2 ]. When applying these pre-trained models on specific targets in various environments, not only does the neural network structures need to be changed often, but a series of image preprocessing is also required to provide a more precise model [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

A Deep Dive of Autoencoder Models on Low-Contrast Aquatic Images

Lee

Chen

2021

Sensors

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Public aquariums and similar institutions often use video as a method to monitor the behavior, health, and status of aquatic organisms in their environments. These video footages take up a sizeable amount of space and require the use of autoencoders to reduce their file size for efficient storage. The autoencoder neural network is an emerging technique which uses the extracted latent space from an input source to reduce the image size for storage, and then reconstructs the source within an acceptable loss range for use. To meet an aquarium’s practical needs, the autoencoder must have easily maintainable codes, low power consumption, be easily adoptable, and not require a substantial amount of memory use or processing power. Conventional configurations of autoencoders often provide results that perform beyond an aquarium’s needs at the cost of being too complex for their architecture to handle, while few take low-contrast sources into consideration. Thus, in this instance, “keeping it simple” would be the ideal approach to the autoencoder’s model design. This paper proposes a practical approach catered to an aquarium’s specific needs through the configuration of autoencoder parameters. It first explores the differences between the two of the most widely applied autoencoder approaches, Multilayer Perceptron (MLP) and Convolution Neural Networks (CNN), to identify the most appropriate approach. The paper concludes that while both approaches (with proper configurations and image preprocessing) can reduce the dimensionality and reduce visual noise of the low-contrast images gathered from aquatic video footage, the CNN approach is more suitable for an aquarium’s architecture. As an unexpected finding of the experiments conducted, the paper also discovered that by manipulating the formula for the MLP approach, the autoencoder could generate a denoised differential image that contains sharper and more desirable visual information to an aquarium’s operation. Lastly, the paper has found that proper image preprocessing prior to the application of the autoencoder led to better model convergence and prediction results, as demonstrated both visually and numerically in the experiment. The paper concludes that by combining the denoising effect of MLP, CNN’s ability to manage memory consumption, and proper image preprocessing, the specific practical needs of an aquarium can be adeptly fulfilled.

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“…The correct recognition of image depends upon quality of captured image, lighting condition etc. [4]. Recognition rate can be improved by performing pre-processing on the captured image.…”

Section: Introductionmentioning

confidence: 99%

Spatial Domain Representation for Face Recognition

Goyal¹,

Mukherjee²

2019

Visual Object Tracking With Deep Neural Networks

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Spatial domain representation for face recognition characterizes extracted spatial facial features for face recognition. This chapter provides a complete understanding of well-known and some recently explored spatial domain representations for face recognition. Over last two decades, scale-invariant feature transform (SIFT), histogram of oriented gradients (HOG) and local binary patterns (LBP) have emerged as promising spatial feature extraction techniques for face recognition. SIFT and HOG are effective techniques for face recognition dealing with different scales, rotation, and illumination. LBP is texture based analysis effective for extracting texture information of face. Other relevant spatial domain representations are spatial pyramid learning (SPLE), linear phase quantization (LPQ), variants of LBP such as improved local binary pattern (ILBP), compound local binary pattern (CLBP), local ternary pattern (LTP), three-patch local binary patterns (TPLBP), four-patch local binary patterns (FPLBP). These representations are improved versions of SIFT and LBP and have improved results for face recognition. A detailed analysis of these methods, basic results for face recognition and possible applications are presented in this chapter.

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Improving Face Recognition Rate with Image Preprocessing

Cited by 33 publications

References 21 publications

Assessing the Performance of DWT-PCA/SVD Face Recognition Algorithm under Multiple Constraints

Assessing the Performance of DWT-PCA/SVD Face Recognition Algorithm under Multiple Constraints

A Deep Dive of Autoencoder Models on Low-Contrast Aquatic Images

Spatial Domain Representation for Face Recognition

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