Facial emotion recognition using subband selective multilevel stationary wavelet gradient transform and fuzzy support vector machine

Kumar, Rahul; Sundaram, M.; Arumugam, N.

doi:10.1007/s00371-020-01988-1

Cited by 22 publications

(5 citation statements)

References 39 publications

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“…Each sample trained by a fuzzy support vector machine adds an affiliation term in addition to the sample features and class identification [10]. Let the training sample set be…”

Section: Fuzzy Support Vector Machinesmentioning

confidence: 99%

Speech Emotion Recognition Based on Fuzzy Support Vector Machine

2022

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Language is an important part of everyday information communication, and automatic speech emotion information recognition technology has broad application prospects in education, services and medicine. The purpose of this paper is to study speech emotion recognition by specific fuzzy support vector machines. The applicability of fuzzy theory for solving speech emotion states is investigated and the theory of support vector machines, fuzzy support vector machines, is introduced to analyse speech emotion feature extraction in terms of both short-time energy and resonance peaks. The performance of SVM and FSVM methods are compared using Emo-DB corpus tests and it is found that the FSVM method has a higher recognition rate than the SVM method.

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“…Each sample trained by a fuzzy support vector machine adds an affiliation term in addition to the sample features and class identification [10]. Let the training sample set be…”

Section: Fuzzy Support Vector Machinesmentioning

confidence: 99%

Speech Emotion Recognition Based on Fuzzy Support Vector Machine

2022

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“…(2) e data closer to another type of training center may also become a support vector [17]. (3) In actual applications, the least support vector is obtained, and the greatest capability is obtained [18].…”

Section: Scientific Programmingmentioning

confidence: 99%

Simulation Evaluation of Calligraphy Art Style Relying on Fuzzy Support Vector Machine Scientific Calculation Algorithm

Cai

2022

Scientific Programming

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With the comprehensive follow-up of the information age and the rise of digital technology, the collection of calligraphy art can be preserved in the form of image data. As a testimony to the Chinese 5,000-year history, calligraphy has formed its own genre in the long river of time and its style prevailed in different periods. Nowadays, the recognition technology of characters has become advanced, but there is no better method for the recognition and classification of the artistic style of calligraphy. In addition, there are more and more counterfeiting methods, and computers alone cannot completely replace human identification; therefore, the two need to be integrated and cooperated in the identification work. This article proposes an improved fuzzy support vector machine algorithm based on the aggregation algorithm, which uses the skeleton extraction of calligraphic fonts as the main method. It uses the extracted font style and font morphological characteristics to identify the style of the work and whether it is genuine. In the article, the improvement strategy of the traditional fuzzy support vector machine is proposed, and its detection accuracy is compared with that of the traditional fuzzy support vector machine. The experimental results of this article show that the discrimination rate of authenticity cannot reach 100% and there is a certain degree of error. Through repeated experiments for many times, the value has a certain change, and when the step size is 0.1, calculating the average value can get the value of the false rejection rate and the false recognition rate.

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“…Saurav et al [44] proposed Dual Integrated Convolution Neural Network (DICNN) model for recognizing 'in the wild' facial expressions on embedded platform. Jeen et al [25] utilized subband selective multilevel stationary wavelet gradient transform features for recognizing facial expressions. Image filter-based Subspace Learning (IFSL) is proposed by Yan et al [65] for better capturing the facial information.…”

Section: Related Workmentioning

confidence: 99%

“…6a, b. (25). The process of feature extraction through RCP 2 for a numerical example is demonstrated in Fig.…”

Section: Feature Extraction Through Rcpmentioning

confidence: 99%

Modified chess patterns: handcrafted feature descriptors for facial expression recognition

Kartheek

Prasad

Bhukya

2021

Complex Intell. Syst.

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Facial expressions are predominantly important in the social interaction as they convey the personal emotions of an individual. The main task in Facial Expression Recognition (FER) systems is to develop feature descriptors that could effectively classify the facial expressions into various categories. In this work, towards extracting distinctive features, Radial Cross Pattern (RCP), Chess Symmetric Pattern (CSP) and Radial Cross Symmetric Pattern (RCSP) feature descriptors have been proposed and are implemented in a 5 $$\times $$ × 5 overlapping neighborhood to overcome some of the limitations of the existing methods such as Chess Pattern (CP), Local Gradient Coding (LGC) and its variants. In a 5 $$\times $$ × 5 neighborhood, the 24 pixels surrounding the center pixel are arranged into two groups, namely Radial Cross Pattern (RCP), which extracts two feature values by comparing 16 pixels with the center pixel and Chess Symmetric Pattern (CSP) extracts one feature value from the remaining 8 pixels. The experiments are conducted using RCP and CSP independently and also with their fusion RCSP using different weights, on a variety of facial expression datasets to demonstrate the efficiency of the proposed methods. The results obtained from the experimental analysis demonstrate the efficiency of the proposed methods.

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Facial emotion recognition using subband selective multilevel stationary wavelet gradient transform and fuzzy support vector machine

Cited by 22 publications

References 39 publications

Speech Emotion Recognition Based on Fuzzy Support Vector Machine

Speech Emotion Recognition Based on Fuzzy Support Vector Machine

Simulation Evaluation of Calligraphy Art Style Relying on Fuzzy Support Vector Machine Scientific Calculation Algorithm

Modified chess patterns: handcrafted feature descriptors for facial expression recognition

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