Haryati Jaafar | Universiti Sains Malaysia (original) (raw)
Papers by Haryati Jaafar
Occurrence of embolism from the patients who are suffering from carotid artery stenosis may lead ... more Occurrence of embolism from the patients who are suffering from carotid artery stenosis may lead to the onset of stroke once it becomes more severe. The Doppler ultrasound technique is commonly used to detect emboli in the cerebral circulation. However, the detection of emboli is still relying on human as an observer as a gold standard. A classification system was proposed in this study to detect emboli based on principle of ultrasound signal. There were 12 coefficients from Mel Frequency Cepstral Coefficient (MFCC) and 14 coefficients from Linear Prediction Coefficient (LPC) were extracted as the emboli features of the investigation. The classification of microembolus was performed by using the Support Vector Machine (SVM) classifier. The experiments were done on 3 patients with different number of training and testing samples. It is shown that the features extracted from the LPC method achieved better classification performance of 83.04% than those of MFCC method with only 81.95%. Therefore it revealed the feasibility of an automated detection of emboli feature from ultrasound signal.
Occurrence of embolism from the patients who are suffering from carotid artery stenosis may lead ... more Occurrence of embolism from the patients who are suffering from carotid artery stenosis may lead to the onset of stroke once it becomes more severe. The Doppler ultrasound technique is commonly used to detect emboli in the cerebral circulation. However, the detection of emboli is still relying on human as an observer as a gold standard. A classification system was proposed in this study to detect emboli based on principle of ultrasound signal. There were 12 coefficients from Mel Frequency Cepstral Coefficient (MFCC) and 14 coefficients from Linear Prediction Coefficient (LPC) were extracted as the emboli features of the investigation. The classification of microembolus was performed by using the Support Vector Machine (SVM) classifier. The experiments were done on 3 patients with different number of training and testing samples. It is shown that the features extracted from the LPC method achieved better classification performance of 83.04% than those of MFCC method with only 81.95%. Therefore it revealed the feasibility of an automated detection of emboli feature from ultrasound signal.
International Journal of Computational Science and Engineering
Biometric is a technology for verification or identification of individuals by employing a person... more Biometric is a technology for verification or identification of individuals by employing a person's physiological and behavioural traits. Although these systems are more secured compared the traditional methods such as key, smart card or password, they also undergo with many limitations such as noise in sensed data, intra-class variations and spoof attacks. One of the solutions to these problems is by implementing multibiometric systems where in these systems, many sources of biometric information are used. This paper presents a review of multibiometric systems including its taxonomy, the fusion level schemes and toward the implementation of fixed and adaptive weighting fusion schemes so as to sustain the effectiveness of executing the multibiometric systems in real application.
In this paper an Intelligent Frog Species Identification System (IFSIS) which works as a sensor i... more In this paper an Intelligent Frog Species Identification System (IFSIS) which works as a sensor is developed. It is designed to assist the nonexperts to recognize frog species according to frog bioacoustics signals for environmental monitoring. IFSIS consists of Android devices and a server. Android device is used to record frog call signal and to display the details of the detected frog species once the identification is processed by the server. Meanwhile, feature extraction and identification process of the frog call signal are done on Intel atom board which works as server. The Mel Frequency Cepstrum Coefficient (MFCC) is used as feature extraction technique while the classifier employed is Support Vector Machine (SVM). Experimental results show that the performances of 95.33% has been achieved which proves that IFSIS can be a viable automated tool for recognizing frog species.
Journal of Computer Science
Problem statement: Fusion weight tuning based on score reliability is imperative in order to ensu... more Problem statement: Fusion weight tuning based on score reliability is imperative in order to ensure the performances of multibiometric systems are sustained. Approach: In this study, two variant of conditions i.e., different performances of individual subsystems and inconsistent quality of test samples are experimented to multibiometric systems. By applying multialgorithm scheme, two types of features extraction method i.e., Linear Predictive Coding (LPC) and Mel Frequency Cepstrum Coefficient (MFCC) are executed in this study. Support Vector Machine (SVM) is used as a classifier for both subsystems for the pattern matching process. Scores from both LPC and MFCC based sub systems are fused at score level fusion using fixed weighting and adaptive weighting approaches. For fixed weighting, sum-rule method is employed while for the adaptive weighting, sum-rule based on weight adaptation and sum-rule with weight produced from fuzzy logic inference are executed. The performances of singl...
scientists have discovered that most frog species produce skin secretions of an amino acid compou... more scientists have discovered that most frog species produce skin secretions of an amino acid compound called peptides that can produce several avenues of research with application for human medicine. Instead of depending on physical observation procedure to identify the particular species, this study proposes an automated frog identification system based on bioacoustic signal analysis. Experimental studies of 1260 audio data from 28 species of frogs from the Internet and Intelligent Biometric Group, Universiti Sains Malaysia, IBG, USM databases are used in this study. These audio data are then corrupted by 10dB and 5dB noise. A syllable feature extraction method i.e. Mel-Frequency Cepstrum Coefficients (MFCC) employed to extract the segmented signal. Subsequently, three classifiers i.e. Support Vector Machine (SVM), Sparse Representation Classifier (SRC) and Local Mean k-Nearest Neighbor with Fuzzy Distance Weighting (LMkNN-FDW) are developed in order to evaluate the performance of th...
Automatic recognition of frog sound according to particular species is considered a worthy tool f... more Automatic recognition of frog sound according to particular species is considered a worthy tool for biological research and environmental monitoring. As a result, automatic recognition of frog sound offers many advantages rather than manual method that depending on physical observation procedure. This study evaluates the accuracy of frog sound identification from 12 species that recorded from Malaysia forest. By applying short time energy and short time average zero crossing rate, the frog sound samples are automatically segmented into syllables. A syllable feature extraction method i.e, Mel-Frequency Cepstrum Coefficients is employed to extract the segmented signal. Finally, nonparametric k-nearest neighbor classifier with Euclidean distance has been employed to recognize the frog species. A comparison between automatic segmentation and manual segmentation is applied and results show that automatic segmentation outperforms to identify the frog species with an accuracy of 97% compar...
Automatic recognition of frog sound according to particular species is considered a worthy tool f... more Automatic recognition of frog sound according to particular species is considered a worthy tool for biological research and environmental monitoring. In addition, the synthesis of peptides with antimicrobial activity found in the skin of certain frog species is valuable for medical values. As a result, automatic recognition of frog sound offers many advantages rather than manual method that depending on physical observation procedure. This study evaluate the accuracy of frog sound identification from 10 species that recorded from Malaysia forest located at Sungai Sedim, Kulim and Lata Mengkuang, Baling. By applying frequency information technique, the frog sound samples are automatically segmented into syllables. Two types of syllable feature extraction method i.e, MFCC and LPC are the determined. Finally, nonparametric kNN classifier with Euclidean distance and Chebyshev distance has been employed to recognize the frog species. Results show that kNN classifier based on MFCC and LPC...
2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2014), 2014
2013 IEEE International Conference on Signal and Image Processing Applications, 2013
Identification of frog sound is useful tool and competent in biological research and environmenta... more Identification of frog sound is useful tool and competent in biological research and environmental monitoring. In contrast with traditional methods that not practical due to the time consuming, expensive or detrimental to the animal's welfare, this study proposes an automatic frog call identification system. 750 data species that recorded from Malaysia forest is used as data signals and have been corrupted by lOdB and 20dB noise to determine the performance of accuracy in noisy environment. MFCC parameter is employed as feature extraction. An analysis of signals for different number of MFCCs (8, 12, 15, 20 and 25) is presented and the results are provided using MFCC, Delta Coefficients (�MFCC) and Delta Delta Coefficients (��MFCC). Subsequently, kNN classifier is applied to evaluate the performance in the frog identification system. The results show the accuracy range from 84.67% to 85.78%, 61.33% to 68.89% and 59.33% to 67.33% in clean environment, lOdB and 20dB, respectively.
International Journal, 2011
Abstract-In recent years email has become one of the fastest and most economical means of communi... more Abstract-In recent years email has become one of the fastest and most economical means of communication. However increase of email users has resulted in the dramatic increase of spam emails during the past few years. Data mining -classification algorithms are used to ...
Frog identification based on the vocalization becomes important for biological research and envir... more Frog identification based on the vocalization becomes important for
biological research and environmental monitoring. As a result, different types of
feature extractions and classifiers have been employed. Yet, the k-nearest neighbor
(kNN) is one of the popular classifiers and has been applied in various applications.
This paper proposes an improvement of kNN in order to evaluate the
accuracy of frog sound identification. The recorded sounds of 12 frog species
obtained in Malaysia forest have been segmented using short time energy and short
time average zero crossing rate while the features are extracted by mel frequency
cepstrum coefficient. Finally, a proposed classifier based on local means kNN and
fuzzy distance weighting have been employed to identify the frog species.
Comparison of the system performances based on kNN, local means kNN and the
proposed classifier i.e. fuzzy kNN with manual segmentation and automatic
segmentation is evaluated. The results show the proposed classifier outperforms
the baseline classifier with accuracy of 94.67 % and 98.33 % for manual and
automatic segmentation, respectively.
The automatic frog sound identification system is one of the most useful approaches to assist exp... more The automatic frog sound identification system is one of the most useful approaches to assist experts in identifying frog species and to replace manual techniques claimed to be costly and time-consuming. However, to execute an automatic system in a noisy environment due to background noise is a challenging task. Hence, more robust syllable segmentation techniques are required. In this paper, a combination of enhanced starting and end points detection namely short time energy (STE) and short time average zero crossing rates (STAZCR) is proposed to improve the syllable segmentation. There were fifteen frog species from the Malaysian forest were employed in this study. To validate the performance of the STE and STAZCR, a comparison of the syllable segmentation techniques based on time-frequency domain i..e. sinusoidal modelling (SM) and time domain i.e. Energy and Zero Crossing Rate (E+ZCR) were employed. The experimental results demonstrated that the STE+STAZCR technique is able to obtain 96.27% performance compared to the other techniques i.e. SM and E+ZCR which only achieved 88.53% and 89.97% respectively.
A b s t r a c t Frog identification based on their calls becomes important for biological researc... more A b s t r a c t Frog identification based on their calls becomes important for biological research and environmental monitoring. However, identifying particular frog calls becomes challenging particularly when the frog calls are interrupted with noises either in natural background noise or man-made noise. Hence, an automatic identification frog call system that robust in noisy environment has been proposed in this paper. Experimental studies of 675 audio obtained from 15 species of frogs in the Malaysian forest and recorded in an outdoor environment are used in this study. These audio data are then corrupted by 10dB and 5dB noise. A syllable segmentation technique i.e. short time energy (STE) and Short Time Average Zero Crossing Rate (STAZCR) and feature extraction, Mel-Frequency Cepstrum Coefficients (MFCC) are employed to segment the desired syllables and extract the segmented signal. Subsequently, the Local Mean k-Nearest Neighbor with Fuzzy Distance Weighting (LMkNN-FDW) are employed as a classifier in order to evaluate the performance of the identification system. The experimental results show both of natural background noise and man-made noise outperform by 95.2% and 88.27% in clean SNR, respectively.
In this paper, a new approach for personal identification using finger vein image is presented. F... more In this paper, a new approach for personal identification using finger vein image is presented. Finger vein is an emerging type of biometrics that attracts attention of researchers in biometrics area. As compared to other biometric traits such as face, fingerprint and iris, finger vein is more secured and hard to counterfeit since the features are inside the human body. So far, most of the researchers focus on how to extract robust features from the captured vein images. Not much research was conducted on the classification of the extracted features. In this paper, a new classifier called fuzzy-based k-nearest centroid neighbor (FkNCN) is applied to classify the finger vein image. The proposed FkNCN employs a surrounding rule to obtain the k-nearest centroid neighbors based on the spatial distributions of the training images and their distance to the test image. Then, the fuzzy membership function is utilized to assign the test image to the class which is frequently represented by the k-nearest centroid neighbors. Experimental evaluation using our own database which was collected from 492 fingers shows that the proposed FkNCN has better performance than the k-nearest neighbor, k-nearest-centroid neighbor and fuzzy-based-k-nearest neighbor classifiers. This shows that the proposed classifier is able to identify the finger vein image effectively.
Mobile implementation is a current trend in biometric design. This paper proposes a new approach ... more Mobile implementation is a current trend in biometric design. This paper proposes a new approach to palm print recognition, in which smart phones are used to capture palm print images at a distance. A touchless system was developed because of public demand for privacy and sanitation. Robust hand tracking, image enhancement, and fast computation processing algorithms are required for effective touchless and mobile-based recognition. In this project, hand tracking and the region of interest (ROI) extraction method were discussed. A sliding neighborhood operation with local histogram equalization, followed by a local adaptive thresholding or LHEAT approach, was proposed in the image enhancement stage to manage low-quality palm print images. To accelerate the recognition process, a new classifier, improved fuzzy-based k nearest centroid neighbor (IFkNCN), was implemented. By removing outliers and reducing the amount of training data, this classifier exhibited faster computation. Our experimental results demonstrate that a touchless palm print system using LHEAT and IFkNCN achieves a promising recognition rate of 98.64%.
Conference Presentations by Haryati Jaafar
Microembolus signals (MES) detected by transcranial Doppler (TCD) ultrasound are similar to the s... more Microembolus signals (MES) detected by transcranial Doppler (TCD) ultrasound are similar to the short duration transient signals. In previous researches, an embolus was tracked by using a supervised technique to discriminate the embolus from the background. However, the classification results were found to be affected by many factors and limited under experimental setup conditions. Therefore, a detection system based on the k-means clustering technique (unsupervised learning) is proposed for emboli detection. In order to verify the proposed technique, the signal data sets are also be computed and compared with SVM classifier. The features selected are the measured embo-lus-to-blood ratio (MEBR), peak embolus-to-blood ratio (PEBR) and statistical features. Five independent data sets of different transmitted frequency, probe location and different depths are identified to evaluate the feasibility of this new proposed method. The overall result show that k-means is better than SVM in term of robustness aspect. This work also revealed the feasibility of the automatic detection of the features-based emboli in which it is very imperative in assisting the experts to monitor the stroke patients.
Occurrence of embolism from the patients who are suffering from carotid artery stenosis may lead ... more Occurrence of embolism from the patients who are suffering from carotid artery stenosis may lead to the onset of stroke once it becomes more severe. The Doppler ultrasound technique is commonly used to detect emboli in the cerebral circulation. However, the detection of emboli is still relying on human as an observer as a gold standard. A classification system was proposed in this study to detect emboli based on principle of ultrasound signal. There were 12 coefficients from Mel Frequency Cepstral Coefficient (MFCC) and 14 coefficients from Linear Prediction Coefficient (LPC) were extracted as the emboli features of the investigation. The classification of microembolus was performed by using the Support Vector Machine (SVM) classifier. The experiments were done on 3 patients with different number of training and testing samples. It is shown that the features extracted from the LPC method achieved better classification performance of 83.04% than those of MFCC method with only 81.95%. Therefore it revealed the feasibility of an automated detection of emboli feature from ultrasound signal.
Occurrence of embolism from the patients who are suffering from carotid artery stenosis may lead ... more Occurrence of embolism from the patients who are suffering from carotid artery stenosis may lead to the onset of stroke once it becomes more severe. The Doppler ultrasound technique is commonly used to detect emboli in the cerebral circulation. However, the detection of emboli is still relying on human as an observer as a gold standard. A classification system was proposed in this study to detect emboli based on principle of ultrasound signal. There were 12 coefficients from Mel Frequency Cepstral Coefficient (MFCC) and 14 coefficients from Linear Prediction Coefficient (LPC) were extracted as the emboli features of the investigation. The classification of microembolus was performed by using the Support Vector Machine (SVM) classifier. The experiments were done on 3 patients with different number of training and testing samples. It is shown that the features extracted from the LPC method achieved better classification performance of 83.04% than those of MFCC method with only 81.95%. Therefore it revealed the feasibility of an automated detection of emboli feature from ultrasound signal.
International Journal of Computational Science and Engineering
Biometric is a technology for verification or identification of individuals by employing a person... more Biometric is a technology for verification or identification of individuals by employing a person's physiological and behavioural traits. Although these systems are more secured compared the traditional methods such as key, smart card or password, they also undergo with many limitations such as noise in sensed data, intra-class variations and spoof attacks. One of the solutions to these problems is by implementing multibiometric systems where in these systems, many sources of biometric information are used. This paper presents a review of multibiometric systems including its taxonomy, the fusion level schemes and toward the implementation of fixed and adaptive weighting fusion schemes so as to sustain the effectiveness of executing the multibiometric systems in real application.
In this paper an Intelligent Frog Species Identification System (IFSIS) which works as a sensor i... more In this paper an Intelligent Frog Species Identification System (IFSIS) which works as a sensor is developed. It is designed to assist the nonexperts to recognize frog species according to frog bioacoustics signals for environmental monitoring. IFSIS consists of Android devices and a server. Android device is used to record frog call signal and to display the details of the detected frog species once the identification is processed by the server. Meanwhile, feature extraction and identification process of the frog call signal are done on Intel atom board which works as server. The Mel Frequency Cepstrum Coefficient (MFCC) is used as feature extraction technique while the classifier employed is Support Vector Machine (SVM). Experimental results show that the performances of 95.33% has been achieved which proves that IFSIS can be a viable automated tool for recognizing frog species.
Journal of Computer Science
Problem statement: Fusion weight tuning based on score reliability is imperative in order to ensu... more Problem statement: Fusion weight tuning based on score reliability is imperative in order to ensure the performances of multibiometric systems are sustained. Approach: In this study, two variant of conditions i.e., different performances of individual subsystems and inconsistent quality of test samples are experimented to multibiometric systems. By applying multialgorithm scheme, two types of features extraction method i.e., Linear Predictive Coding (LPC) and Mel Frequency Cepstrum Coefficient (MFCC) are executed in this study. Support Vector Machine (SVM) is used as a classifier for both subsystems for the pattern matching process. Scores from both LPC and MFCC based sub systems are fused at score level fusion using fixed weighting and adaptive weighting approaches. For fixed weighting, sum-rule method is employed while for the adaptive weighting, sum-rule based on weight adaptation and sum-rule with weight produced from fuzzy logic inference are executed. The performances of singl...
scientists have discovered that most frog species produce skin secretions of an amino acid compou... more scientists have discovered that most frog species produce skin secretions of an amino acid compound called peptides that can produce several avenues of research with application for human medicine. Instead of depending on physical observation procedure to identify the particular species, this study proposes an automated frog identification system based on bioacoustic signal analysis. Experimental studies of 1260 audio data from 28 species of frogs from the Internet and Intelligent Biometric Group, Universiti Sains Malaysia, IBG, USM databases are used in this study. These audio data are then corrupted by 10dB and 5dB noise. A syllable feature extraction method i.e. Mel-Frequency Cepstrum Coefficients (MFCC) employed to extract the segmented signal. Subsequently, three classifiers i.e. Support Vector Machine (SVM), Sparse Representation Classifier (SRC) and Local Mean k-Nearest Neighbor with Fuzzy Distance Weighting (LMkNN-FDW) are developed in order to evaluate the performance of th...
Automatic recognition of frog sound according to particular species is considered a worthy tool f... more Automatic recognition of frog sound according to particular species is considered a worthy tool for biological research and environmental monitoring. As a result, automatic recognition of frog sound offers many advantages rather than manual method that depending on physical observation procedure. This study evaluates the accuracy of frog sound identification from 12 species that recorded from Malaysia forest. By applying short time energy and short time average zero crossing rate, the frog sound samples are automatically segmented into syllables. A syllable feature extraction method i.e, Mel-Frequency Cepstrum Coefficients is employed to extract the segmented signal. Finally, nonparametric k-nearest neighbor classifier with Euclidean distance has been employed to recognize the frog species. A comparison between automatic segmentation and manual segmentation is applied and results show that automatic segmentation outperforms to identify the frog species with an accuracy of 97% compar...
Automatic recognition of frog sound according to particular species is considered a worthy tool f... more Automatic recognition of frog sound according to particular species is considered a worthy tool for biological research and environmental monitoring. In addition, the synthesis of peptides with antimicrobial activity found in the skin of certain frog species is valuable for medical values. As a result, automatic recognition of frog sound offers many advantages rather than manual method that depending on physical observation procedure. This study evaluate the accuracy of frog sound identification from 10 species that recorded from Malaysia forest located at Sungai Sedim, Kulim and Lata Mengkuang, Baling. By applying frequency information technique, the frog sound samples are automatically segmented into syllables. Two types of syllable feature extraction method i.e, MFCC and LPC are the determined. Finally, nonparametric kNN classifier with Euclidean distance and Chebyshev distance has been employed to recognize the frog species. Results show that kNN classifier based on MFCC and LPC...
2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2014), 2014
2013 IEEE International Conference on Signal and Image Processing Applications, 2013
Identification of frog sound is useful tool and competent in biological research and environmenta... more Identification of frog sound is useful tool and competent in biological research and environmental monitoring. In contrast with traditional methods that not practical due to the time consuming, expensive or detrimental to the animal's welfare, this study proposes an automatic frog call identification system. 750 data species that recorded from Malaysia forest is used as data signals and have been corrupted by lOdB and 20dB noise to determine the performance of accuracy in noisy environment. MFCC parameter is employed as feature extraction. An analysis of signals for different number of MFCCs (8, 12, 15, 20 and 25) is presented and the results are provided using MFCC, Delta Coefficients (�MFCC) and Delta Delta Coefficients (��MFCC). Subsequently, kNN classifier is applied to evaluate the performance in the frog identification system. The results show the accuracy range from 84.67% to 85.78%, 61.33% to 68.89% and 59.33% to 67.33% in clean environment, lOdB and 20dB, respectively.
International Journal, 2011
Abstract-In recent years email has become one of the fastest and most economical means of communi... more Abstract-In recent years email has become one of the fastest and most economical means of communication. However increase of email users has resulted in the dramatic increase of spam emails during the past few years. Data mining -classification algorithms are used to ...
Frog identification based on the vocalization becomes important for biological research and envir... more Frog identification based on the vocalization becomes important for
biological research and environmental monitoring. As a result, different types of
feature extractions and classifiers have been employed. Yet, the k-nearest neighbor
(kNN) is one of the popular classifiers and has been applied in various applications.
This paper proposes an improvement of kNN in order to evaluate the
accuracy of frog sound identification. The recorded sounds of 12 frog species
obtained in Malaysia forest have been segmented using short time energy and short
time average zero crossing rate while the features are extracted by mel frequency
cepstrum coefficient. Finally, a proposed classifier based on local means kNN and
fuzzy distance weighting have been employed to identify the frog species.
Comparison of the system performances based on kNN, local means kNN and the
proposed classifier i.e. fuzzy kNN with manual segmentation and automatic
segmentation is evaluated. The results show the proposed classifier outperforms
the baseline classifier with accuracy of 94.67 % and 98.33 % for manual and
automatic segmentation, respectively.
The automatic frog sound identification system is one of the most useful approaches to assist exp... more The automatic frog sound identification system is one of the most useful approaches to assist experts in identifying frog species and to replace manual techniques claimed to be costly and time-consuming. However, to execute an automatic system in a noisy environment due to background noise is a challenging task. Hence, more robust syllable segmentation techniques are required. In this paper, a combination of enhanced starting and end points detection namely short time energy (STE) and short time average zero crossing rates (STAZCR) is proposed to improve the syllable segmentation. There were fifteen frog species from the Malaysian forest were employed in this study. To validate the performance of the STE and STAZCR, a comparison of the syllable segmentation techniques based on time-frequency domain i..e. sinusoidal modelling (SM) and time domain i.e. Energy and Zero Crossing Rate (E+ZCR) were employed. The experimental results demonstrated that the STE+STAZCR technique is able to obtain 96.27% performance compared to the other techniques i.e. SM and E+ZCR which only achieved 88.53% and 89.97% respectively.
A b s t r a c t Frog identification based on their calls becomes important for biological researc... more A b s t r a c t Frog identification based on their calls becomes important for biological research and environmental monitoring. However, identifying particular frog calls becomes challenging particularly when the frog calls are interrupted with noises either in natural background noise or man-made noise. Hence, an automatic identification frog call system that robust in noisy environment has been proposed in this paper. Experimental studies of 675 audio obtained from 15 species of frogs in the Malaysian forest and recorded in an outdoor environment are used in this study. These audio data are then corrupted by 10dB and 5dB noise. A syllable segmentation technique i.e. short time energy (STE) and Short Time Average Zero Crossing Rate (STAZCR) and feature extraction, Mel-Frequency Cepstrum Coefficients (MFCC) are employed to segment the desired syllables and extract the segmented signal. Subsequently, the Local Mean k-Nearest Neighbor with Fuzzy Distance Weighting (LMkNN-FDW) are employed as a classifier in order to evaluate the performance of the identification system. The experimental results show both of natural background noise and man-made noise outperform by 95.2% and 88.27% in clean SNR, respectively.
In this paper, a new approach for personal identification using finger vein image is presented. F... more In this paper, a new approach for personal identification using finger vein image is presented. Finger vein is an emerging type of biometrics that attracts attention of researchers in biometrics area. As compared to other biometric traits such as face, fingerprint and iris, finger vein is more secured and hard to counterfeit since the features are inside the human body. So far, most of the researchers focus on how to extract robust features from the captured vein images. Not much research was conducted on the classification of the extracted features. In this paper, a new classifier called fuzzy-based k-nearest centroid neighbor (FkNCN) is applied to classify the finger vein image. The proposed FkNCN employs a surrounding rule to obtain the k-nearest centroid neighbors based on the spatial distributions of the training images and their distance to the test image. Then, the fuzzy membership function is utilized to assign the test image to the class which is frequently represented by the k-nearest centroid neighbors. Experimental evaluation using our own database which was collected from 492 fingers shows that the proposed FkNCN has better performance than the k-nearest neighbor, k-nearest-centroid neighbor and fuzzy-based-k-nearest neighbor classifiers. This shows that the proposed classifier is able to identify the finger vein image effectively.
Mobile implementation is a current trend in biometric design. This paper proposes a new approach ... more Mobile implementation is a current trend in biometric design. This paper proposes a new approach to palm print recognition, in which smart phones are used to capture palm print images at a distance. A touchless system was developed because of public demand for privacy and sanitation. Robust hand tracking, image enhancement, and fast computation processing algorithms are required for effective touchless and mobile-based recognition. In this project, hand tracking and the region of interest (ROI) extraction method were discussed. A sliding neighborhood operation with local histogram equalization, followed by a local adaptive thresholding or LHEAT approach, was proposed in the image enhancement stage to manage low-quality palm print images. To accelerate the recognition process, a new classifier, improved fuzzy-based k nearest centroid neighbor (IFkNCN), was implemented. By removing outliers and reducing the amount of training data, this classifier exhibited faster computation. Our experimental results demonstrate that a touchless palm print system using LHEAT and IFkNCN achieves a promising recognition rate of 98.64%.
Microembolus signals (MES) detected by transcranial Doppler (TCD) ultrasound are similar to the s... more Microembolus signals (MES) detected by transcranial Doppler (TCD) ultrasound are similar to the short duration transient signals. In previous researches, an embolus was tracked by using a supervised technique to discriminate the embolus from the background. However, the classification results were found to be affected by many factors and limited under experimental setup conditions. Therefore, a detection system based on the k-means clustering technique (unsupervised learning) is proposed for emboli detection. In order to verify the proposed technique, the signal data sets are also be computed and compared with SVM classifier. The features selected are the measured embo-lus-to-blood ratio (MEBR), peak embolus-to-blood ratio (PEBR) and statistical features. Five independent data sets of different transmitted frequency, probe location and different depths are identified to evaluate the feasibility of this new proposed method. The overall result show that k-means is better than SVM in term of robustness aspect. This work also revealed the feasibility of the automatic detection of the features-based emboli in which it is very imperative in assisting the experts to monitor the stroke patients.