Synthesis Approach for Emotion Recognition from Cepstral and Pitch Coefficients Using Machine Learning

Vaijayanthi, S.; Arunnehru, J.

doi:10.1007/978-981-33-4909-4_39

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…Several studies [1][2][3]14,15] has been conductedin recent years in the field of recognition of human behavior to reduce the manual effort and increase computational performance. Laptev [16] and Dollar [17] proposed a space-time interest point detector for action recognition and these feature points showed discriminative properties like appearance The biggest challenge in recognizing human activity is identifying and extracting the right and significant features.…”

Section: Related Workmentioning

confidence: 99%

“…From the confusion matrix it is possible to extract a statistical metrics (Precision, Recall, and F-measure) for measuring the performance of classification systems and is defined as follows: Precision (P) or detection rate is a ratio between correctly labelled instances and total labelled instances. It is a percentage of positive predictions in specific class that are correct and it is defined by: precision (P) = TP TP + FP (15) where, TP and FP are the number of true positive and false positive predictions for the particular class. Recall (R) or Sensitivity is a ratio between correctly labeled instances and total instances in the class.…”

Section: Evaluation Metricsmentioning

confidence: 99%

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Machine Vision-Based Human Action Recognition Using Spatio-Temporal Motion Features (STMF) with Difference Intensity Distance Group Pattern (DIDGP)

Arunnehru

Thalapathiraj

Ravikumar³

et al. 2022

Electronics

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In recent years, human action recognition is modeled as a spatial-temporal video volume. Such aspects have recently expanded greatly due to their explosively evolving real-world uses, such as visual surveillance, autonomous driving, and entertainment. Specifically, the spatio-temporal interest points (STIPs) approach has been widely and efficiently used in action representation for recognition. In this work, a novel approach based on the STIPs is proposed for action descriptors i.e., Two Dimensional-Difference Intensity Distance Group Pattern (2D-DIDGP) and Three Dimensional-Difference Intensity Distance Group Pattern (3D-DIDGP) for representing and recognizing the human actions in video sequences. Initially, this approach captures the local motion in a video that is invariant to size and shape changes. This approach extends further to build unique and discriminative feature description methods to enhance the action recognition rate. The transformation methods, such as DCT (Discrete cosine transform), DWT (Discrete wavelet transforms), and hybrid DWT+DCT, are utilized. The proposed approach is validated on the UT-Interaction dataset that has been extensively studied by past researchers. Then, the classification methods, such as Support Vector Machines (SVM) and Random Forest (RF) classifiers, are exploited. From the observed results, it is perceived that the proposed descriptors especially the DIDGP based descriptor yield promising results on action recognition. Notably, the 3D-DIDGP outperforms the state-of-the-art algorithm predominantly.

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