Live Detection of Face Using Machine Learning with Multi-feature Method

Kumar, Sandeep; Singh, Sukhwinder; Kumar, Jagdish

doi:10.1007/s11277-018-5913-0

Cited by 32 publications

(9 citation statements)

References 25 publications

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“…The algorithms and methods which are based on the Segmentation of Speech are:  Wavelet Method: This method [49][50][51] is used to identify the beginnings and ends of the phonemes which are based on the analysis of DWT [52] and, with the aid of spectral analysis, an extremely effective technique has occurred in the form result [53].  Artificial Neural Networks: An ANN is based on an artificial neuron sequence of linked units or knots, which loosely from the neurons of a biological brain [54]. The signal can be transmitted from each connection by another neuron, like synapses within a neural network of Brain [55].…”

Section: B Various Classifiers Based On Classificationmentioning

confidence: 99%

“…MATLAB 7.8 implemented the technique. The proposed approach was implemented and analyzed for different Punjabi speech signals [50][51][52][53][54][55][56][57].  Hybrid Speech Segmentation Algorithm: In the Segmentation of Hybrid Speech algorithm, the features were extracted to determine the limits of the term using a basic threshold methodology.…”

Section: B Various Classifiers Based On Classificationmentioning

confidence: 99%

“…Loss is the loss function used for the network. [47], [53][54][55][56]. This is a kind of accelerated science since every guess is a test of what we think we know and every answer is feedback so that we know how wrong we are.…”

Section: → − = +mentioning

confidence: 99%

“…Classification of speech recognition is done based on different methods and algorithms used in their processing techniques and introducing more techniques and methods of enhancement to identify speech gradually improved the outcome. Speech Enhancement, Speech Classification, Speech Segmentation and Speech Extraction based on the features of Speech Recognizing techniques are the key identification goals listed in this paper [53][54], [78][79]. Recognition of speech is further distinguished as Recognition of Speakers and Language Identification which is achieved using a few techniques for speech processing.…”

Section: A Classification Of Speech Processingmentioning

confidence: 99%

See 3 more Smart Citations

An Appraisal on Speech and Emotion Recognition Technologies based on Machine Learning

Kumar¹,

Jason²

2020

IJRTE

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In earlier days, people used speech as a means of communication or the way a listener is conveyed by voice or expression. But the idea of machine learning and various methods are necessary for the recognition of speech in the matter of interaction with machines. With a voice as a bio-metric through use and significance, speech has become an important part of speech development. In this article, we attempted to explain a variety of speech and emotion recognition techniques and comparisons between several methods based on existing algorithms and mostly speech-based methods. We have listed and distinguished speaking technologies that are focused on specifications, databases, classification, feature extraction, enhancement, segmentation and process of Speech Emotion recognition in this paper

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Section: B Various Classifiers Based On Classificationmentioning

confidence: 99%

Section: B Various Classifiers Based On Classificationmentioning

confidence: 99%

Section: → − = +mentioning

confidence: 99%

Section: A Classification Of Speech Processingmentioning

confidence: 99%

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An Appraisal on Speech and Emotion Recognition Technologies based on Machine Learning

Kumar¹,

Jason²

2020

IJRTE

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“…Among them, the local features also include texture, LBP, color, etc. [4][5][6], which are used to depict the facial details; while the global features are used to portray overall information of face, for instance, the principal component analysis (PCA), the kernel PCA, etc., can extract facial features. Both global or local features can only describe partial face and capture fragments of face information, so that it is impossible for them to fully characterize the facial information [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A Face Recognition Algorithm Based on Optimal Feature Selection

Zhao¹,

Wang²,

Wang³

2019

RIA

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To achieve accurate and robust face recognition, this paper designs a face recognition algorithm based on optimal feature selection. The algorithm is denoted as GRA-LSSVM, because it integrates grey relational analysis (GRA) with least squares support vector machine (LSSVM). Firstly, the target face image was segmented into several subblocks. Next, the global features of the face were extracted from each subblock by kernel principal component analysis (PCA). After that, the GRA algorithm was introduced to determine the features that contribute greatly to face recognition. These features were integrated into an eigenvector. Finally, a face classifier by the LSSVM based on the "one-to-many" principle, and simulated with multiple face databases. The simulation shows the GRA-LSSVM derived the optimal feature subset for face recognition, and thus outperformed other face recognition algorithms in accuracy and speed. The research provides an effective and advanced method for face recognition.

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