Efficient Gabor-Based Recognition for Handwritten Arabic-Indic Digits

Jaha, Emad Sami

doi:10.14569/ijacsa.2019.0100114

Cited by 10 publications

(5 citation statements)

References 25 publications

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“…The classification process is performed by measuring the distance between feature vectors of training and testing samples in feature space. The k -nearest samples with their class labels are then retrieved to choose the predominant class label as a class for the test sample [ 29 ]. In this work, we tested different distance metrics with different odd k numbers, where the distance measures used in KNN are Euclidean and Manhattan distances, which can be defined as given in Equations (8) and (9), respectively [ 30 ].…”

Section: Proposed Methodologymentioning

confidence: 99%

Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination

Alwagdani,

Jaha

2023

Sensors

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Handwritten Arabic character recognition has received increasing research interest in recent years. However, as of yet, the majority of the existing handwriting recognition systems have only focused on adult handwriting. In contrast, there have not been many studies conducted on child handwriting, nor has it been regarded as a major research issue yet. Compared to adults’ handwriting, children’s handwriting is more challenging since it often has lower quality, higher variation, and larger distortions. Furthermore, most of these designed and currently used systems for adult data have not been trained or tested for child data recognition purposes or applications. This paper presents a new convolution neural network (CNN) model for recognizing children’s handwritten isolated Arabic letters. Several experiments are conducted here to investigate and analyze the influence when training the model with different datasets of children, adults, and both to measure and compare performance in recognizing children’s handwritten characters and discriminating their handwriting from adult handwriting. In addition, a number of supplementary features are proposed based on empirical study and observations and are combined with CNN-extracted features to augment the child and adult writer-group classification. Lastly, the performance of the extracted deep and supplementary features is evaluated and compared using different classifiers, comprising Softmax, support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF), as well as different dataset combinations from Hijja for child data and AHCD for adult data. Our findings highlight that the training strategy is crucial, and the inclusion of adult data is influential in achieving an increased accuracy of up to around 93% in child handwritten character recognition. Moreover, the fusion of the proposed supplementary features with the deep features attains an improved performance in child handwriting discrimination by up to around 94%.

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Section: Proposed Methodologymentioning

confidence: 99%

Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination

Alwagdani,

Jaha

2023

Sensors

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“…Gabor filters have been extensively used for feature extraction, due to their capabilities for analyzing the visual appearance of an image, and extracting discriminative feature vectors [29]. It has been used and confirmed to be useful in several biometric applications, such as face detection or recognition, iris recognition, and fingerprint recognition [30]. Gabor filters have achieved high accuracy in face recognition in many research studies, as reviewed in Zeng et al [25] even when faces were partially occluded.…”

Section: Gabor-based Featuresmentioning

confidence: 99%

“…Since the large number of the extracted Gabor features by all 14 filters from a normalized image of size 64 × 64 pixels, an effective dimensionality reduction method, proposed in Jaha [30], was initially applied on the whole extracted Gabor features. This method is designed to address the resulting 14 filtered images (concatenated in their same positions as shown in Fig.…”

Section: Gabor-based Featuresmentioning

confidence: 99%

Dynamic Feature Subset Selection for Occluded Face Recognition

Alsaedi¹,

Jaha²

2022

Intelligent Automation &Amp; Soft Computing

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Accurate recognition of person identity is a critical task in civil society for various application and different needs. There are different well-established biometric modalities that can be used for recognition purposes such as face, voice, fingerprint, iris, etc. Recently, face images have been widely used for person recognition, since the human face is the most natural and user-friendly recognition method. However, in real-life applications, some factors may degrade the recognition performance, such as partial face occlusion, poses, illumination conditions, facial expressions, etc. In this paper, we propose two dynamic feature subset selection (DFSS) methods to achieve better recognition for occluded faces. The proposed DFSS methods are based on resilient algorithms attempting to minimize the negative influence of confusing and low-quality features extracted from occluded areas by either exclusion or weight reduction. Principal Component Analysis and Gabor filtering based approaches are initially used for face feature extraction, then the proposed DFSS methods are dynamically enforced. This is leading to more effective feature representation and an improved recognition performance. To validate their effectiveness, multiple experiments are conducted and the performance of different methods is compared. The experimental work is carried out using AR database and Extended Yale Face Database B. The obtained results of face identification and verification show that both proposed DFSS methods outperform the standard (static) use of the whole number of features and the equal feature weights.

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“…• Gabor-based features: Gabor features have attracted considerable attention and achieved enormous success in many face recognition purposes due to their capabilities for analyzing the visual appearance of an image and extracting discriminative feature vectors [51,52]. Gabor filters have been used and confirmed to be useful in several biometric applications, including face detection or recognition, iris recognition, and fingerprint recognition [53]. There are several research studies, as reviewed in [47], where Gabor based algorithms have achieved high accuracies in occluded face recognition.…”

Section: Feature Extraction 431 Face Feature Extractionmentioning

confidence: 99%

“…Gabor filters-based feature extraction methods are usually computationally expensive due to the high dimensionality in calculation. Hence, the dimensionality of the whole extracted feature vector was reduced using an effective dimensionality reduction method proposed in [53]. This method was designed to address a single large image comprising all 14 concatenated filtered images.…”

Section: Feature Extraction 431 Face Feature Extractionmentioning

confidence: 99%

Dynamic Audio-Visual Biometric Fusion for Person Recognition

Ghalandari¹,

Mahariq²,

Ghadak³

et al. 2022

Computers, Materials &Amp; Continua

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Biometric recognition refers to the process of recognizing a person's identity using physiological or behavioral modalities, such as face, voice, fingerprint, gait, etc. Such biometric modalities are mostly used in recognition tasks separately as in unimodal systems, or jointly with two or more as in multimodal systems. However, multimodal systems can usually enhance the recognition performance over unimodal systems by integrating the biometric data of multiple modalities at different fusion levels. Despite this enhancement, in real-life applications some factors degrade multimodal systems' performance, such as occlusion, face poses, and noise in voice data. In this paper, we propose two algorithms that effectively apply dynamic fusion at feature level based on the data quality of multimodal biometrics. The proposed algorithms attempt to minimize the negative influence of confusing and low-quality features by either exclusion or weight reduction to achieve better recognition performance. The proposed dynamic fusion was achieved using face and voice biometrics, where face features were extracted using principal component analysis (PCA), and Gabor filters separately, whilst voice features were extracted using Mel-Frequency Cepstral Coefficients (MFCCs). Here, the facial data quality assessment of face images is mainly based on the existence of occlusion, whereas the assessment of voice data quality is substantially based on the calculation of signal to noise ratio (SNR) as per the existence of noise. To evaluate the performance of the proposed algorithms, several experiments were conducted using two combinations of three different databases, AR database, and the extended Yale Face Database B for face images, in addition to VOiCES database for voice data. The obtained results show that both proposed dynamic fusion algorithms attain improved performance and offer more advantages in identification and verification over not only the standard unimodal algorithms but also the multimodal algorithms using standard fusion methods.

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Efficient Gabor-Based Recognition for Handwritten Arabic-Indic Digits

Cited by 10 publications

References 25 publications

Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination

Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination

Dynamic Feature Subset Selection for Occluded Face Recognition

Dynamic Audio-Visual Biometric Fusion for Person Recognition

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