Detecting facial emotions using normalized minimal feature vectors and semi-supervised twin support vector machines classifier

Kumar, Manoj Prabhakaran; Rajagopal, Manoj Kumar

doi:10.1007/s10489-019-01500-w

Cited by 18 publications

(6 citation statements)

References 36 publications

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“…Corporate fraud detection [88] Text classification [89] Sentiment analysis [90] K-nearest neighbor Bankruptcy prediction [91] Market manipulation [92] Decision trees Corporate Fraud detection [93] Bankruptcy prediction [94] Random forest Stock market investment [95] Credit fraud detection [96] Support vector machines Bankruptcy prediction [97] Facial emotions detection [98] Recurrent neural network Bankruptcy prediction [99] Sentiment analysis [100]…”

Section: Naive Bayesmentioning

confidence: 99%

Artificial Intelligence in Accounting and Finance: Challenges and Opportunities

Yi,

Cao,

Chen

et al. 2023

IEEE Access

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The rapid expansion of artificial intelligence (AI) technologies presents novel technical solutions to traditional accounting and finance problems. Despite this, scholars in accounting and finance frequently encounter difficulties navigating the extensive and intricate domain knowledge of AI and its continuously evolving literature. To address this gap, this paper conducts a qualitative survey of the implementation of AI methods in accounting and finance. The paper is structured into four sections. Firstly, we examine the conventional accounting and finance issues and their requirement for AI techniques. Secondly, to inform accounting and finance researchers about the potential of AI, we present broad categories of AI applications. Thirdly, we explore recent research on AI solutions to conventional problems. Finally, we highlight emerging trends and possible research directions.

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Section: Naive Bayesmentioning

confidence: 99%

Artificial Intelligence in Accounting and Finance: Challenges and Opportunities

Yi,

Cao,

Chen

et al. 2023

IEEE Access

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“…Given the facial images, their designed FER system first detects faces using a novel unsupervised technique based on the active contour (AC) model. The FER pipeline proposed by Kumar and Rajagopal [43] has used normalized minimal feature vectors and semi-supervised Twin Support Vector Machine (TWSVM) learning. Li and Wen [44], proposed a sample awareness-based personalized (SAP) FER method that uses the Bayesian learning method to select the optimal classifier from the global perspective and then used the selected classifier to identify the emotional class of each test sample.…”

Section: Related Workmentioning

confidence: 99%

Facial Expression Recognition Using Dynamic Local Ternary Patterns With Kernel Extreme Learning Machine Classifier

2021

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Rapid growth in advanced human-computer interaction (HCI) based applications has led to the immense popularity of facial expression recognition (FER) research among computer vision and pattern recognition researchers. Lately, a robust texture descriptor named Dynamic Local Ternary Pattern (DLTP) developed for face liveness detection has proved to be very useful in preserving facial texture information. The findings motivated us to investigate DLTP in more detail and examine its usefulness in the FER task. To this end, a FER pipeline is developed, which uses a sequence of steps to detect possible facial expressions in a given input image. Given an input image, the pipeline first locates and registers faces in it. In the next step, using an image enhancement operator, the FER pipeline enhances the facial images. Afterward, from the enhanced images, facial features are extracted using the DLTP descriptor. Subsequently, the pipeline reduces dimensions of the high-dimensional DLTP features via Principal Component Analysis (PCA). Finally, using the multi-class Kernel Extreme Learning Machine (K-ELM) classifier, the proposed FER scheme classifies the features into facial expressions. Extensive experiments performed on four in-the-lab and one in-the-wild FER datasets confirmed the superiority of the method. Besides, the cross-dataset experiments performed on different combinations of the FER datasets revealed its robustness. Comparison results with several state-of-the-art FER methods demonstrate the usefulness of the proposed FER scheme. The pipeline with a recognition accuracy of 99.76%, 99.72%, 93.98%, 96.71%, and 78.75%, respectively, on the CK+, RaF, KDEF, JAFFE, and RAF-DB datasets, outperformed the previous state-of-the-art.INDEX TERMS Facial expression recognition, dynamic local ternary pattern, principal component analysis, kernel extreme learning machine, cross-dataset, cross-validation.

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“…Kumar and Rajagopal [80] proposed Multi-class TSVM for detecting human face happiness combined with Constrained Local Model. Authors [81] also proposed semisupervised multi TSVM to predict human facial emotions with 13 minimal features that can detect six basic human emotions. Algorithm achieved highest accuracy and least computation time with minimal feature vectors.…”

Section: Applications Of Twin Support Vector Classificationmentioning

confidence: 99%

Comprehensive Review On Twin Support Vector Machines

Tanveer,

Rajani,

Rastogi

et al. 2021

Preprint

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Twin support vector machine (TSVM) and twin support vector regression (TSVR) are newly emerging efficient machine learning techniques which offer promising solutions for classification and regression challenges respectively. TSVM is based upon the idea to identify two nonparallel hyperplanes which classify the data points to their respective classes. It requires to solve two small sized quadratic programming problems (QPPs) in lieu of solving single large size QPP in support vector machine (SVM) while TSVR is formulated on the lines of TSVM and requires to solve two SVM kind problems. Although there has been good research progress on these techniques; there is limited literature on the comparison of different variants of TSVR. Thus, this review presents a rigorous analysis of recent research in TSVM and TSVR simultaneously mentioning their limitations and advantages. To begin with we first introduce the basic theory of TSVM and then focus on the various improvements and applications of TSVM, and then we introduce TSVR and its various enhancements. Finally, we suggest future research and development prospects.

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Detecting facial emotions using normalized minimal feature vectors and semi-supervised twin support vector machines classifier

Cited by 18 publications

References 36 publications

Artificial Intelligence in Accounting and Finance: Challenges and Opportunities

Artificial Intelligence in Accounting and Finance: Challenges and Opportunities

Facial Expression Recognition Using Dynamic Local Ternary Patterns With Kernel Extreme Learning Machine Classifier

Comprehensive Review On Twin Support Vector Machines

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