scholarly journals A robust high speed personal identification system for augled faces

2001 ◽  
Vol 121 (11) ◽  
pp. 1740-1746
Author(s):  
Teruaki Hirano ◽  
Osamu Nakamura
2022 ◽  
Vol 17 ◽  
pp. 34-41
Author(s):  
R Arthi ◽  
D Manojkumar ◽  
Aksa Abraham ◽  
Allada Rahul Kishan ◽  
Alekhya Sattenapalli

Multi-biometric system is an advanced technology which has a wide application space in the field of information security. This work proposes the design of a personal identification system based on a combination of biometric inputs such as face, finger vein, iris and fingerprint. Viola jones algorithm is used for face detection. Convolutional neural network (CNN) with different optimisers are used to steeply raise the image texture and extract high definition distinct features of the input images. The deep dream image algorithm accompanies CNN by visualizing these images and by highlighting the image features learnt by the network. These images are used for understanding and diagnosing network behaviour. This network obtains a high recognition rate, which proves to be better performing than traditional algorithms. In addition to these, a high-speed advanced wireless communication technology (Li-Fi) is used in combination with GSM which would act as an alert system that effectively helps in notifying the supervisory authority, if the system is being trespassed without proper authentication.


2012 ◽  
Vol 468-471 ◽  
pp. 920-923
Author(s):  
Ya Ping Bao ◽  
Li Liu ◽  
Yuan Wang ◽  
Qian Song

This paper introduced a fast fingerprint identification system based on TMS320VC5416 DSP chip and MBF200 solidity fingerprint sensor. It precipitates fingerprint identification device developing into the direction of miniaturization, embedded and automatic.It recommends fingerprint identification system hardware and software design and the main system processing flow, aim at fingerprint identification arithmetic, the influence of system operation speed is being researched at the same time. High-speed data acquisition system is been built in order to achieve a DSP fingerprint identification system with high efficiency and low cost.


2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
Phaklen EhKan ◽  
Timothy Allen ◽  
Steven F. Quigley

In today's society, highly accurate personal identification systems are required. Passwords or pin numbers can be forgotten or forged and are no longer considered to offer a high level of security. The use of biological features, biometrics, is becoming widely accepted as the next level for security systems. Biometric-based speaker identification is a method of identifying persons from their voice. Speaker-specific characteristics exist in speech signals due to different speakers having different resonances of the vocal tract. These differences can be exploited by extracting feature vectors such as Mel-Frequency Cepstral Coefficients (MFCCs) from the speech signal. A well-known statistical modelling process, the Gaussian Mixture Model (GMM), then models the distribution of each speaker's MFCCs in a multidimensional acoustic space. The GMM-based speaker identification system has features that make it promising for hardware acceleration. This paper describes the hardware implementation for classification of a text-independent GMM-based speaker identification system. The aim was to produce a system that can perform simultaneous identification of large numbers of voice streams in real time. This has important potential applications in security and in automated call centre applications. A speedup factor of ninety was achieved compared to a software implementation on a standard PC.


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