Preprocessing techniques of facial image with Median and Gabor filters

Hemalatha, G.; Sumathi, C. P.

doi:10.1109/icices.2016.7518860

Cited by 17 publications

(6 citation statements)

References 2 publications

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“…For dimensionality reduction proposed Eigen-PCA has been used. This is based on the general PCA with the Eigen face concept [17]. For feature extraction, proposed deep learning with optimization algorithm called local binary pattern network enhanced with PSO [18].…”

Section: Proposed Methodologymentioning

confidence: 99%

“…The adding noise will make the image textured and affect the recognition system. Median filter [17] has been too used here to improve the image with color normalization and reduction of noise.…”

Section: Face Image Preprocessingmentioning

confidence: 99%

“…The training process was continued, and the parameters were optimized with the help of the Eq. (17).…”

Section: Classification Using Deep Belief Networkmentioning

confidence: 99%

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Face Recognition System Using Deep Belief Network and Particle Swarm Optimization

Babu¹,

Kumar²,

Kannaiyaraju³

2022

Intelligent Automation &Amp; Soft Computing

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Facial expression for different emotional feelings makes it interesting for researchers to develop recognition techniques. Facial expression is the outcome of emotions they feel, behavioral acts, and the physiological condition of one's mind. In the world of computer visions and algorithms, precise facial recognition is tough. In predicting the expression of a face, machine learning/artificial intelligence plays a significant role. The deep learning techniques are widely used in more challenging real-world problems which are highly encouraged in facial emotional analysis. In this article, we use three phases for facial expression recognition techniques. The principal component analysis-based dimensionality reduction techniques are used with Eigen face value for edge detection. Then the feature extraction is performed using swarm intelligence-based grey wolf with particle swarm optimization techniques. The neural network is highly used in deep learning techniques for classification. Here we use a deep belief network (DBN) for classifying the recognized image. The proposed method's results are assessed using the most comprehensive facial expression datasets, including RAF-DB, AffecteNet, and Cohn-Kanade (CK+). This developed approach improves existing methods with the maximum accuracy of 94.82%, 95.34%, 98.82%, and 97.82% on the test RAF-DB, AFfectNet, CK+, and FED-RO datasets respectively.

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Section: Proposed Methodologymentioning

confidence: 99%

Section: Face Image Preprocessingmentioning

confidence: 99%

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Face Recognition System Using Deep Belief Network and Particle Swarm Optimization

Babu¹,

Kumar²,

Kannaiyaraju³

2022

Intelligent Automation &Amp; Soft Computing

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“…Pada penelitian sebelumnya, deteksi objek melalui CCTV hanya menggunakan penentuan region of *) penulis korespondensi: Saluky Email: saluky@syekhnurjati.ac.id ISSN: 2477-5126 e-ISSN: 2548-9356 Saluky: Penerapan Histogram Normalisasi untuk… 189 interest (ROI) untuk mengurangi kebisingan di sekitar objek yang dideteksi [10], [11], dengan cara ini kontrasi dari frame masih belum ditingkatkan sehingga masih banyak informasi yang terlewatkan karena keterbatasan deteksi. Penelitian ini bertujuan untuk mengevaluasi efektivitas metode normalisasi histogram dalam meningkatkan kontras pada video dan membandingkannya dengan metode peningkatan kontras lainnya [12], [13]. Studi ini akan melibatkan aplikasi metode normalisasi histogram pada video yang diperoleh dari sistem pengawasan CCTV dan hasil akan dibandingkan dengan video sebelum diterapkan normalisasi histogram.…”

Section: Pendahuluan Peningkatan Kontras Pencahayaan Dengan Menggunakanunclassified

Penerapan Normalisasi Histogram untuk Peningkatan Kontras Pencahayaan pada Pengamatan Visual CCTV

Saluky,

Marine,

Bahiyah

2023

JPIT

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Low Contrast can cause low image quality and make it difficult for proper image analysis. One technique to improve image quality is to increase the lighting contrast. One method that is often used is histogram normalization, which can increase image contrast by balancing the distribution of pixels across a range of pixel values. The purpose of this research is to apply the histogram normalization method to images and compare the results before and after the normalization process. The images used in this study are self-made images and images from public databases. The results of the study show that normalized histograms can increase image contrast and improve low image quality due to inadequate lighting. Thus, histogram normalization can be used as a technique to improve image quality in various applications, including medical image processing, satellite image processing, and security surveillance.

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“…The filter acts as a window that sweeps throughout the image pixels, arranging the pixels beneath this window in an ascending order before taking the midpoint and assume it to be the output pixel. [16] This will result in spreading the light evenly across the image. Median filtering can be mathematically expressed as follows: [17] y…”

Section: Preprocessing Stepmentioning

confidence: 99%

Exploiting Wavelet Transform, Principal Component Analysis, Support Vector Machine, and K-Nearest Neighbors for Partial Face Recognition

Alhabib

Al-Dabagh²,

Almukhtar

et al. 2019

Cihan U Erbil SCI J

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Facial analysis has evolved to be a process of considerable importance due to its consequence on the safety and security, either individually or generally on the society level, especially in personal identification. The paper in hand applies facial identification on a facial image dataset by examining partial facial images before allocating a set of distinctive characteristics to them. Extracting the desired features from the input image is achieved by means of wavelet transform. Principal component analysis is used for feature selection, which specifies several aspects in the input image; these features are fed to two stages of classification using a support vector machine and K-nearest neighborhood to classify the face. The images used to test the strength of the suggested method are taken from the well-known (Yale) database. Test results showed the eligibility of the system when it comes to identify images and assign the correct face and name.

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Preprocessing techniques of facial image with Median and Gabor filters

Cited by 17 publications

References 2 publications

Face Recognition System Using Deep Belief Network and Particle Swarm Optimization

Face Recognition System Using Deep Belief Network and Particle Swarm Optimization

Penerapan Normalisasi Histogram untuk Peningkatan Kontras Pencahayaan pada Pengamatan Visual CCTV

Exploiting Wavelet Transform, Principal Component Analysis, Support Vector Machine, and K-Nearest Neighbors for Partial Face Recognition

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