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Comparison of computer-based and opt...

Alorf, Abdulaziz A.

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Comparison of computer-based and optical face recognition paradigms.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Comparison of computer-based and optical face recognition paradigms./
作者:	Alorf, Abdulaziz A.
面頁冊數:	254 p.
附註:	Source: Masters Abstracts International, Volume: 53-02.
Contained By:	Masters Abstracts International53-02(E).
標題:	Engineering, Electronics and Electrical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1557388
ISBN:	9781303947315

Comparison of computer-based and optical face recognition paradigms.
Alorf, Abdulaziz A.

Comparison of computer-based and optical face recognition paradigms. - 254 p.

Source: Masters Abstracts International, Volume: 53-02.

Thesis (M.S.)--Michigan Technological University, 2014.

The main objectives of this thesis are to validate an improved principal components analysis (IPCA) algorithm on images; designing and simulating a digital model for image compression, face recognition and image detection by using a principal components analysis (PCA) algorithm and the IPCA algorithm; designing and simulating an optical model for face recognition and object detection by using the joint transform correlator (JTC); establishing detection and recognition thresholds for each model; comparing between the performance of the PCA algorithm and the performance of the IPCA algorithm in compression, recognition and, detection; and comparing between the performance of the digital model and the performance of the optical model in recognition and detection. The MATLAB(c) software was used for simulating the models.

ISBN: 9781303947315Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Comparison of computer-based and optical face recognition paradigms.
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100 1 $a Alorf, Abdulaziz A. $3 2103940
245 1 0 $a Comparison of computer-based and optical face recognition paradigms.
300 $a 254 p.
500 $a Source: Masters Abstracts International, Volume: 53-02.
500 $a Adviser: Michael C. Roggemann.
502 $a Thesis (M.S.)--Michigan Technological University, 2014.
520 $a The main objectives of this thesis are to validate an improved principal components analysis (IPCA) algorithm on images; designing and simulating a digital model for image compression, face recognition and image detection by using a principal components analysis (PCA) algorithm and the IPCA algorithm; designing and simulating an optical model for face recognition and object detection by using the joint transform correlator (JTC); establishing detection and recognition thresholds for each model; comparing between the performance of the PCA algorithm and the performance of the IPCA algorithm in compression, recognition and, detection; and comparing between the performance of the digital model and the performance of the optical model in recognition and detection. The MATLAB(c) software was used for simulating the models.
520 $a PCA is a technique used for identifying patterns in data and representing the data in order to highlight any similarities or differences. The identification of patterns in data of high dimensions (more than three dimensions) is too difficult because the graphical representation of data is impossible. Therefore, PCA is a powerful method for analyzing data. IPCA is another statistical tool for identifying patterns in data. It uses information theory for improving PCA. The joint transform correlator (JTC) is an optical correlator used for synthesizing a frequency plane filter for coherent optical systems.
520 $a The IPCA algorithm, in general, behaves better than the PCA algorithm in the most of the applications. It is better than the PCA algorithm in image compression because it obtains higher compression, more accurate reconstruction, and faster processing speed with acceptable errors; in addition, it is better than the PCA algorithm in real-time image detection due to the fact that it achieves the smallest error rate as well as remarkable speed. On the other hand, the PCA algorithm performs better than the IPCA algorithm in face recognition because it offers an acceptable error rate, easy calculation, and a reasonable speed. Finally, in detection and recognition, the performance of the digital model is better than the performance of the optical model.
590 $a School code: 0129.
650 4 $a Engineering, Electronics and Electrical. $3 626636
650 4 $a Physics, Optics. $3 1018756
650 4 $a Engineering, Robotics. $3 1018454
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710 2 $a Michigan Technological University. $b Electrical & Computer Engineering. $3 1044744
773 0 $t Masters Abstracts International $g 53-02(E).
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791 $a M.S.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1557388