Spin-Image Descriptors for Text-Independent Speaker Recognition

Mohammed, Suhaila; Jabir, Adnan J.; Abbas, Zaid Ali

doi:10.1007/978-3-030-33582-3_21

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…Most applications utilize non-overlapped block processing to extract local features. However, overlapped block processing increases the recognition accuracy [67][68][69]. Thus, in this paper, overlapped block processing is performed.…”

Section: Methodsmentioning

confidence: 99%

Face Recognition Algorithm Based on Fast Computation of Orthogonal Moments

et al. 2022

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Face recognition is required in various applications, and major progress has been witnessed in this area. Many face recognition algorithms have been proposed thus far; however, achieving high recognition accuracy and low execution time remains a challenge. In this work, a new scheme for face recognition is presented using hybrid orthogonal polynomials to extract features. The embedded image kernel technique is used to decrease the complexity of feature extraction, then a support vector machine is adopted to classify these features. Moreover, a fast-overlapping block processing algorithm for feature extraction is used to reduce the computation time. Extensive evaluation of the proposed method was carried out on two different face image datasets, ORL and FEI. Different state-of-the-art face recognition methods were compared with the proposed method in order to evaluate its accuracy. We demonstrate that the proposed method achieves the highest recognition rate in different considered scenarios. Based on the obtained results, it can be seen that the proposed method is robust against noise and significantly outperforms previous approaches in terms of speed.

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Section: Methodsmentioning

confidence: 99%

Face Recognition Algorithm Based on Fast Computation of Orthogonal Moments

et al. 2022

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“…The slide image is firstly converted into grey scale version before contrast enhancement process. Given the image pixel ( , ) , the minimum and maximum intensity values in , the enhanced pixel ( , ) can be calculated using the following equation [11]:…”

Section: Contrast Enhancementmentioning

confidence: 99%

“…By dividing each entry in the matrix by the total number of RLM entries, a joint conditional probability density function p (i, j) can be obtained from the matrix. The five characteristics below are generated from the RLM of each window [11]: 1. Short-Run Emphasis (SRE): If there are so many short runs in the window, this attribute will be high.…”

Section: Local Feature Extractionmentioning

confidence: 99%

Intelligent System for Parasitized Malaria Infection Detection Using Local Descriptors

Ali¹,

Mohammed²

2021

IJIES

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Malaria is a curative disease, with therapeutics available for patients, such as drugs that can prevent future malaria infections in countries vulnerable to malaria. Though, there is no effective malaria vaccine until now, although it is an interesting research area in medicine. Local descriptors of blood smear image are exploited in this paper to solve parasitized malaria infection detection problem. Swarm intelligence is used to separate the red blood cells from the background of the blood slide image in adaptive manner. After that, the effective corner points are detected and localized using Harris corner detection method. Two types of local descriptors are generated from the local regions of the effective corners which are Gabor based features and color based features. The extracted features are finally fed to Deep Belief Network (DBN) for classification purpose. Different tests were performed and different combinations of feature types are attempted. The achieved results showed that when using combined vectors of local descriptors, the system gives the desired accuracy which is 100%. The achieved result demonstrates the effectiveness of using local descriptors in solving malaria infection detection problem.

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“…After that, windowing process is applied to reduce the effect of dis-connectivity at the ends of each frame. 2) Each frame is then transformed from the time domain into the frequency domain by incorporating Short Term Fourier Transform (STFT) using the following equation [11]:…”

Section: Spectrogram Image Constructionmentioning

confidence: 99%

Speech Emotion Recognition Using MELBP Variants of Spectrogram Image

Mohammed¹,

Alia²

2020

IJIES

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Speech emotion recognition finds many applications in the daily life like conversational agents, human robot interaction, call centres etc. However; the task of emotion recognition from speech signal is not trivial due to the difficulty in determining the effective feature set that can recognize the emotion conveyed within the signal in an accurate manner. Image processing techniques are exploited in this paper to solve speech emotion recognition problem. After converting the signal into 2D spectrogram image representation, four forms of Extended Local Binary Pattern (ELBP) are generated to serve as a source for feature extraction stage. The histograms of multiple blocks from ELBP variants are computed and fed to Deep Belief Network (DBN) for classification purpose. Different tests were performed using Surrey AudioVisual Expressed Emotion (SAVEE) database and the achieved results showed that when using combined vectors of MELBP, the system gives the best accuracy which is 72.14%. The achieved result outperforms state-of-the-art results on the same database.

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Spin-Image Descriptors for Text-Independent Speaker Recognition

Cited by 4 publications

References 7 publications

Face Recognition Algorithm Based on Fast Computation of Orthogonal Moments

Face Recognition Algorithm Based on Fast Computation of Orthogonal Moments

Intelligent System for Parasitized Malaria Infection Detection Using Local Descriptors

Speech Emotion Recognition Using MELBP Variants of Spectrogram Image

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