Mohd Norzali Haji Mohd

Publications (12) View all

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  Available from: Mohd Norzali Haji Mohd

  Dataset: 'Halal' Logo Detection and Recognition System

  Mohd Norzali, Hj Mohd, Mohd Helmy, Abd Wahab, Amir Bin Yaakob
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  ABSTRACT: Illegal and unapproved 'Halal' logo has been widely used by many unscrupulous producers on their products. Consequently, Muslim consumers become confused in deciding whether a product is carrying a legal 'Halal' logo or otherwise. This paper reports the use of an image detection and recognition system in overcoming the problem. This system is an essential module for the user warning assistance and it contains two main modules; detection and recognition module. The images of 'Halal' logo were capture by using a digital camera. The images were taken from various product surfaces such as metal, plastic and glass. Then 'Halal' logo images were detected in order to load the images manually to the recognition system. After doing preprocessing process on the samples of 'Halal' logo images, it shows that Gaussian Blur effect give a good impression on the detection time. Therefore, it is the most suitable techniques for detection system to detect and crop 'Halal' image properly. From the observation based on the result, Gaussian blur technique state about 85.71% in successfully crop the image compared to normal image, 19.05% and brightness and contrast effect, 47.62%. In the recognition system, Neural Networks methods were used to recognize and classify the images. It is a suitable technique in solving such complex problems. Neural network were fed by 2500 bits of 1's and 0's. In order to increase the recognition system performance, it's depends on how the Neural Network was trained and many sets of binary logo should be used in the system.
• Dataset: Fusion of Radio Frequency Identification (RFID) and Fingerprint in Boarding School Monitoring System (BoSs)

  Abdul Herdawatie, Mohd Kadir, Abd Helmy, Zarina Wahab, Mohd Tukiran, Mohd Razali, Mohd Tomari, Mohd Norzali
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  Available from: Mohd Norzali Haji Mohd

  Dataset: Fusion of Radio Frequency Identification (RFID) and Fingerprint in Boarding School Monitoring System (BoSs)

  Abdul Herdawatie, Mohd Kadir, Abd Helmy, Zarina Wahab, Mohd Tukiran, Mohd Razali, Mohd Tomari, Mohd Norzali
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  Available from: Mohd Norzali Haji Mohd

  Dataset: Thermal-Visual Facial Feature Extraction Based on Nostril Mask

  Mohd Norzali, Haji Mohd, Masayuki Kashima, Kiminori Sato, Mutsumi Watanabe
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  ABSTRACT: This paper aims to present facial features extraction by integrating 2 different sensors that will be used in the estimation of internal mental state. Thermal infrared and visible camera are being used in the stimulus experiment by measuring three facial areas of sympathetic importance which is periorbital, supraorbital and maxillary through purely imaging means in thermal infrared spectrums. The development of Automatic Thermal Face, Supraorbital, Periorbital, Maxillary and Nostril Detection to be used for estimation of internal state is also presented. Several faces samples were taken in real time in our experimental setup to measure the effectiveness of our method. Almost 98% of correct measurement of ROI and temperature was detected. In this paper, a new method for detecting facial feature in both thermal and visual is also presented by applying Nostril Mask, which allows one to find facial feature namely nose area in thermal and visual. Graph Cut algorithm is applied to remove unwanted ROI and correctly detect precise temperature values. Extraction of thermal-visual facial feature images is done by using Scale Invariant Feature Transform (SIFT) Feature detector and extractor to verify the method of using nostril mask. Based on the experiment conducted, it shows 88.6% of correct matching.
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  Available from: Mohd Norzali Haji Mohd

  Article: Effective Geometric Calibration and Facial Feature Extraction Using Multi Sensors

  MOHD NORZALI Haji Mohd, Masayuki KASHIMA, Kiminori SATO, Mutsumi WATANABE
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  ABSTRACT: This paper aims to present facial feature extraction by integrating 3 different sensors that might be used in the estimation of internal mental state. RGB-D camera is used at the pre and post monitoring phase while thermal infrared and visible camera is being used in the stimulus experiment. The measurement of three facial areas of sympathetic importance through purely imaging means that is periorbital, supraorbital and maxillary is done on the second stage. An Accurate and efficient thermal-infrared camera calibration is important for advancing computer vision research approach for geometrically calibrating individual and multiple cameras in both thermal and visible modalities. We also propose new printed Fever Cold Plaster (FCP) chessboard using a popular existing approach which is comparatively accurate and simple to execute. Based on the experiment conducted by comparing the degradation of image quality with the current approach, our proposed chessboard can be more clearly located than those on the applied standard chessboard by 39%
  International Journal of Engineering Science and Innovative Technology. 11/2012; 1(2-2):170-178.