Haryati Jaafar
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Preferred name
Haryati Jaafar
Official Name
Jaafar, Haryati
Alternative Name
Jaafar, Haryati
Jaafar, H.
Main Affiliation
Scopus Author ID
55357649900
Researcher ID
ILC-1943-2023

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  • Publication
    Character segmentation for automatic vehicle license plate recognition based on fast k-means clustering
    ( 2020-11-09)
    Ariff F.N.M.
    ;
    Aimi Salihah Abdul Nasir
    ;
    Haryati Jaafar
    ;
    Zulkifli A.N.
    Automatic vehicle license plate recognition (AVLPR) system is one of application for transportation area under intelligent transport system. This system helps in monitor and identify the vehicle by reading the vehicles license plate numbers and recognize the plate characters automatically. However, various factors such as diversity of plate character viewpoint, shape, format and unstable light conditions at the time of image acquisition were obtained, have challenged the system to segment and recognize the characters. Therefore, this paper, presents an effective procedure approached based on fast k-mean (FKM) clustering. FKM approached have an ability to shortening the time of the image cluster centers process consumed. In addition, the FKM algorithm also able to overcomes the cluster center re-processing problem when constantly added the image in huge quantities. The proposed procedure begins with enhancing the input image by using modified white patch and converted into grayscale image. A total of 100 of images has been tested for the segmentation process with clustering techniques approach used. Template matching is used to standardize the recognition results obtained. The highest achieved was 88.57% of average accuracy for FKM clustering technique compared to k-means clustering where it was only able to achieve an average accuracy of 85.78% and 86.14% for fuzzy c-means. Thus, this show that the most efficient, quicker and more useful algorithm goes to FKM rather than the algorithm for fuzzy c-means (FCM) and k-means (KM). Therefore, it is possible toward consider the proposed FKM clustering as an image segmentation method for segmenting license plate images.
      6  17
  • Publication
    An Evaluation of Fuzzy in Image Enhancement: Design and Comparison for Penicillium and Aspergillus Species
    ( 2024-08-01)
    Zabani F.N.
    ;
    Haryati Jaafar
    ;
    Radzuan N.R.R.M.
    ;
    Ariff F.N.M.
    ;
    Baharum A.
    The main focus in this study is to enhance and classify the image of a type of filamentous fungi named Penicillium and Aspergillus. For image enhancement, fuzzy-partition gamma adaptive histogram equalization (FpGAHE) is proposed to improve the quality of an image, in particular the low quality of a microscopic image. Two stages have been considered in this technique. In the first stage, a fuzzy partition is developed to handle the inconsistency of the gray level values of the images by introducing a fuzzy set. In the second stage, surrounding neighborhood is employed to avoid the imbalance data and reduce the drastically changes of brightness values of the image. The performances are evaluated into two parts i.e., image processing and image classification by using the collected microscopic images of fungi species. To evaluate the effectiveness of the proposed technique, the existing techniques, AHE and AGC is compared to the FpGAHE. In image processing, the result attained shows that the proposed technique has a better performance by obtaining the highest value for the PSNR, SSIM and FSIM evaluation for the species of A. terreus in clean condition. Meanwhile, in image classification, five different nearest neighbor classifiers have been tested. The results show the proposed FpGAHE with Improved Fuzzy-Based k Nearest Centroid Neighbor (IFkNCN) classifier perform the best result compare to other nearest neighbor classifier by obtaining the value of 92.59 and 93.95 for the salt and pepper and Gaussian noise corrupted images respectively.
      1
  • Publication
    An Adaptive Decision Weighted Fusion-based Strategy in Multibiometric System for Face Recognition
    ( 2023-01-01)
    Ariff F.N.M.
    ;
    Haryati Jaafar
    Face recognition system has been foremost since the advent of computers as it involves low-cost installation, non-invasive equipment, and the ease of data collection process. However, the development of face recognition system is not straightforward due to the changes of facial expression, face feature and pose. These changes lead to the performance degradation. Therefore, a multibiometric system is explored and an adaptive decision weighted fusion-based strategy is proposed to tackle the poor performance of single biometric system. The proposed strategy is divided into two parts. In part one, the adaptive weighted is considered where the weighted value is determine based on distance metric of Euclidean Distance (ED). In part two, the decision of fusion process is employed to select the optimum weightage based on distance that obtained in part one. Two feature extractions which are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) have been employed to investigate the effectiveness of proposed fusion technique in face identification process. In order to evaluate the effectiveness of the proposed strategy, the face image of benchmark database and collected database are used for the performance evaluations. The results show an adaptive decision weighted fusion-based strategy comes out as an outstanding fusion technique compared to other tested rules with the performances of 95.33% is achieved from the benchmark database and collected database, respectively.
      4  21