scholarly journals Face Recognition Algorithm based on Doubly Truncated Gaussian Mixture Model using DCT Coefficients

2012 ◽  
Vol 39 (9) ◽  
pp. 23-28 ◽  
Author(s):  
D. Haritha ◽  
Ch. Satyanaraya K. Srinivasa Rao
Keyword(s):  
Face Recognition ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Recognition Algorithm ◽  
Dct Coefficients ◽  
Truncated Gaussian

scholarly journals Voice and Face Recognition for Web Browser Security

2021 ◽  
Vol 9 (11) ◽  
pp. 199-205
Author(s):  
Reddy Phanidhar Reddy
Keyword(s):  
Face Recognition ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Voice Recognition ◽  
Gaussian Mixture ◽  
Paper Analysis ◽  
Web Browser ◽  
Browser Security ◽  
Encryption Decryption

Abstract: This paper analysis about browser privacy multimodal authentication mechanisms. Face and voice recognition will be used as authentication methods in this process. The OpenCV library is used in the framework's face recognition section. It detects and recognizes faces from a database using basic eigen face recognition approaches. The MFCC (Mel Frequency Cepstrum Coefficients) and Gaussian Mixture Model are used to recognize voices. Following successful authentication, the cookies on the local hard disc are decrypted, allowing us access to the browser cookies. Initially, after a user registers, we will encrypt the browser cookies with AES, one of the most secure encryption methods available. keywords: MFCC, Gaussian Mixture Model, Browser cookies, authentication, AES, encryption, decryption, Open CV, Eigen.

scholarly journals AN OVERVIEW OF THE RECOGNITION ALGORITHM OF A HUMAN VOICE

2021 ◽  
Vol 335 (1) ◽  
pp. 32-38
Author(s):  
O. Mamyrbayev ◽  
A. Karelova
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Data Storage ◽  
Gaussian Mixture ◽  
Recognition Algorithm ◽  
Human Machine Interaction ◽  
Human Voice ◽  
Phone Calls ◽  
Time Distortion ◽  
Dynamic Time

Speech recognition has various applications, including human-machine interaction, sorting phone calls by gender classification, categorizing videos with tags, and so on. Currently, machine learning is a popular field that is widely used in various fields and applications, taking advantage of the latest developments in digital technologies and the advantages of data storage capabilities from electronic media. In this article, we will focus on voice gender recognition for a class of text-dependent systems using the Dynamic time distortion (DTW) algorithm and for a class of text-independent systems, the Gaussian mixture model. With this method, it is possible to distinguish a person's voice with the highest accuracy, since the components of Gaussian mixtures can simulate the personality of the voice. The article presents the results of testing the algorithm, and concludes that the Gaussian mixture model is applicable to solving the problem of identifying a person by voice.

AN OVERVIEW OF THE RECOGNITION ALGORITHM OF A HUMAN VOICE

2021 ◽  
Vol 335 (1) ◽  
pp. 32-38
Author(s):  
O. Mamyrbayev ◽  
O. Mamyrbayev
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Data Storage ◽  
Gaussian Mixture ◽  
Recognition Algorithm ◽  
Human Machine Interaction ◽  
Human Voice ◽  
Phone Calls ◽  
Time Distortion ◽  
Dynamic Time

Speech recognition has various applications, including human-machine interaction, sorting phone calls by gender classification, categorizing videos with tags, and so on. Currently, machine learning is a popular field that is widely used in various fields and applications, taking advantage of the latest developments in digital technologies and the advantages of data storage capabilities from electronic media. In this article, we will focus on voice gender recognition for a class of text-dependent systems using the Dynamic time distortion (DTW) algorithm and for a class of text-independent systems, the Gaussian mixture model. With this method, it is possible to distinguish a person's voice with the highest accuracy, since the components of Gaussian mixtures can simulate the personality of the voice. The article presents the results of testing the algorithm, and concludes that the Gaussian mixture model is applicable to solving the problem of identifying a person by voice.

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